Method for searching a text (or alphanumeric string) database, restructuring and parsing text data (or alphanumeric string), creation/application of a natural language processing engine, and the creation/application of an automated analyzer for the creation of medical reports

ABSTRACT

A sequential series of methods for optimized searching within a text (or alphanumeric string) database to retrieve specific and relevant results, followed by optimized restructuring and parsing of text data (or alphanumeric string), followed by creation/application of a natural language processing engine, followed by the creation/application of an automated analyzer is presented.

PRIOR PROVISIONAL APPLICATION

The present application claims the benefit of U.S. provisionalapplication Ser. No. 61/790,817 filed Mar. 15, 2013, the disclosure ofwhich is hereby incorporated by reference.

FIELD OF THE INVENTION

The present invention relates to a method that allows for thecomprehensive reading and understanding of radiology, clinical,pathology, and laboratory reports, and, more particularly, to asequential series of methods for optimization of keyword searches withina keyword searchable text (or alphanumeric string) database,restructuring and parsing of the data, the creation and application of anatural language processing engine, and the creation and application ofan automated analyzer all for production of easily read and comprehendedmedical reports.

BACKGROUND OF THE INVENTION

The potential of role of informatics in the future of medicine isimmense. Medical leaders have voiced concern and the emerging idea ofperformance indicators for radiology departments will soon become thestandard of practice. These indicators will serve to gauge theperformance of licensed radiology departments, but they will provide noquantification of the work upstream in the medical pipeline: properutilization of radiological imaging. Few individuals have realized thisdeficit in gauging radiology; thus, the research in this area has beenlimited.

The only factors that can be used to gauge utilization of radiology areeconomic and statistical. Intuitively it would seem an economic basedquantification system would be more pragmatic. However, a gauge based onimaging costs and cost-effectiveness is impractical as it cannot beextrapolated across institutions readily. One whole body computedtomography (CT) examination at a particular institution may be billed asa single unit, yet the same exam may be broken into separate billingcodes for chest, abdomen, and pelvis at another institution. Further,the assignment of relative value units (RVU) to individual products orservices and allocation of overhead costs may vary between institutions.Thus, using an economic based quantification system to gauge radiologyutilization across institutions and departments is impractical,inefficient, and inaccurate.

In contrast, gauging the utilization of radiology by a statisticalquantification system lacks the negative factors found in an economicbased system. Specifically, gauging the utilization of radiology bypositive scan rates bypasses the differences created by variations inbilling codes seen in different institutions. For example, a positiveaortic dissection multi-detector CT (MDCT) in one Radiology Departmentwould be considered a positive aortic dissection MDCT in a secondRadiology Department given the proper imaging findings. Therefore,quantifying radiology utilization through imaging protocol positive scanrates is the only viable method to compare use across institutions anddepartments objectively. However, economic based imaging gauges canserve a role for internal or departmental analysis.

Once normalized radiology utilization gauges are created, further stepsinto normalizing and then comparing other downstream metrics becomespossible.

SUMMARY OF THE INVENTION

Each imaging protocol is optimized for a given diagnosis; therefore,each examination performed under a given protocol can be defined aspositive or negative, and sometimes indeterminate, for a particulardiagnosis. The resulting positive scan rate serves as a surrogate markerfor the utilization, or use, of that particular diagnostic imagingprotocol. Complications arise when alternative diagnoses are identifiedby an imaging protocol not designed for that particular diagnosis.Fortunately, this complication can be dealt with by creating a separateoverall positive scan rate for all significant diagnoses identified.

A separate limitation in gauging radiology utilization by positive scanrates exists from the standpoint of appropriateness criteria. Since theinitial investigation was retrospective only, no conclusions can beinferred regarding the satisfaction of clinical appropriateness criteriaset forth by the American College of Radiology (ACR). However, theliterature suggests the criteria are only sporadically used in theclinical setting, partly negating the argument that this be considered alimitation of the prior investigation. Further, positive scan rates mayalso serve as potential surrogate retrospective markers in place ofprospective surveys of the use of appropriateness criteria.

The current operational limitation to gauging the utilization ofradiology through positive scan rates is that the research necessary toproperly identify the positive scan rate for a given imaging protocolneeds to be identified with relative ease and accuracy. Currently, thesetwo requirements have not been met. To date, accurate imaging positivityrates need to be identified through laborious and time-intensiveresearch. The optimal method for identification of imaging positive scanrates has yet to be conceived.

The first objective to create an optimal method was to build a highquality database of non-traumatic emergency (ER) patients with suspectedaortic dissection examined by multi-detector computed tomography (MDCT)during the years 2002 (403 cases), 2003 (579 cases), and 2004 (660cases). Initially, the investigation served to define the trend in theaortic dissection protocol MDCT positive scan rate during the studyperiod. Later, after the database was complete, it served as thereference standard for comparison for further work in gauging radiologyutilization through automation.

The future of gauging radiology utilization will require management oflarge databases derived from multiple radiology departments. Theoriginal goal was to turn the investigation (from 2005 and 2006) into anautomated software-based data analysis and computational tool foridentifying imaging protocol positive scan rates. Given the sheer volumeof data necessary to generate adequate reliability, the potential for aproperly constructed procedure for interpretation analysis is immense.Once constructed, what originally took months to analyze would take lessthan a few minutes. After the database was completed and the naturallanguage processing (NLP) decoding engine method accuracy was assured,subsequent investigation focused on creating increased levels ofautomation at the last/analytical stage.

The following methods for a new 2002, 2003, and 2004 aortic dissectionprotocol MDCT database were invented and validated (in 11/2005 and12/2005) against the previous aortic dissection protocol MDCT databaseof 2002 and 2003 cases as published in the radiology literature (1). Allsubsequent work during the intervening years from 2006 to 2013 focusedon invention of new and efficient formulas to automatically analyze thecreated database and other similar databases: the Automated Analyzer(unpublished databases). The methods described are equally applicable topathology, clinical, and laboratory reports.

From a broad perspective, looking at the additive effect of Stages 1-4,the results transform text data (or alphanumeric string) from radiology,pathology, clinical, and laboratory reports into natural languageunderstanding and automated analytics.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is an illustration of a diagram showing the softwarearchitecture/method stages process flow relative to a standard clinicaldatabase, according to an embodiment of the present invention.

FIG. 2 is an illustration of a diagram showing the relative positions ofphysical hardware related to the software/method stages and the dataflow environment within standard hospital Health Level 7 (HL7) interfacearchitecture, according to an embodiment of the present invention.

FIG. 3 is an illustration of a diagram showing standard imagingworkflow, from imaging order to imaging protocol, according to anembodiment of the present invention.

FIG. 4 is an illustration of a diagram showing standard imagingworkflow, from imaging protocol to imaging report production, accordingto an embodiment of the present invention.

FIG. 5 is an illustration of a diagram showing an example of Stage 1methods from clinical database search to report results, according to anembodiment of the present invention.

FIG. 6 is an example of a screen shot of an advanced query userinterface screen, according to an embodiment of the present invention.

FIG. 7 is an illustration of a diagram showing an example of Stage 2methods from report result to restructuring and parsing into a singlerow within a standard spreadsheet database, according to an embodimentof the present invention.

FIG. 8 is an enlarged simple example screen shot of parsed databaseheader terms for basic understanding.

FIG. 9 is an illustration of a diagram showing an example of successiveStage 2 methods from clinical database search to multiple report results(Stage 1 methods are omitted for simplicity), according to an embodimentof the present invention.

FIG. 10 is an illustration of a diagram showing an example of successiveStage 2 methods from multiple report results in multiple color codedrows of a single column to multiple color coded columns through parsingand/or replication; successive steps transform a column of restructuredand parsed reports into a large color coded database, according to anembodiment of the present invention.

FIG. 11 is an illustration of a diagram showing an example of Stage 3methods applied to multiple reports resulting in multiple color codedrows of a sample two column color coded database, to simplifyunderstanding, according to an embodiment of the present invention.

FIG. 12 is an example of a screen shot of a NLP engine table (dissectionprotocol), according to an embodiment of the present invention.

FIG. 13A is an example of a screen shot of a NLP engine table (POSAAD),according to an embodiment of the present invention.

FIG. 13B is an example of a screen shot of a NLP engine table (POSAAD,cont'd.), according to an embodiment of the present invention.

FIG. 13C is an example of a screen shot of a NLP engine table (POSAAD,cont'd.), according to an embodiment of the present invention.

FIG. 13D is an example of a screen shot of a NLP engine table (POSAAD,cont'd.), according to an embodiment of the present invention.

FIG. 13E is an example of a screen shot of a NLP engine table (POSAAD,cont'd.), according to an embodiment of the present invention.

FIG. 14A is an example of a screen shot of a NLP engine table (Type Aand B), according to an embodiment of the present invention.

FIG. 14B is an example of a screen shot of a NLP engine table (Type Aand B, cont'd.), according to an embodiment of the present invention.

FIG. 15A is an example of a screen shot of a NLP engine table (Type 1,2, and 3), according to an embodiment of the present invention.

FIG. 15B is an example of a screen shot of a NLP engine table (Type 1,2, and 3, cont'd.), according to an embodiment of the present invention.

FIG. 16 is an example of a screen shot of a NLP engine table (CLASS 1),according to an embodiment of the present invention.

FIG. 17A is an example of a screen shot of a NLP engine table (NEGCLASS 1) where NEG refer to Negative, according to an embodiment of thepresent invention.

FIG. 17B is an example of a screen shot of a NLP engine table (NEG CLASS1, cont'd.), according to an embodiment of the present invention.

FIG. 17C is an example of a screen shot of a NLP engine table (NEG CLASS1, cont'd.), according to an embodiment of the present invention.

FIG. 18 is an example of a screen shot of a NLP engine table (CLASS 2),according to an embodiment of the present invention.

FIG. 19A is an example of a screen shot of a NLP engine table (STABLECLASS 2), according to an embodiment of the present invention.

FIG. 19B is an example of a screen shot of a NLP engine table (STABLECLASS 2, cont'd.), according to an embodiment of the present invention.

FIG. 19C is an example of a screen shot of a NLP engine table (STABLECLASS 2, cont'd.), according to an embodiment of the present invention.

FIG. 19D is an example of a screen shot of a NLP engine table (STABLECLASS 2, cont'd.), according to an embodiment of the present invention.

FIG. 19E is an example of a screen shot of a NLP engine table (STABLECLASS 2, cont'd.), according to an embodiment of the present invention.

FIG. 19F is an example of a screen shot of a NLP engine table (STABLECLASS 2, cont'd.), according to an embodiment of the present invention.

FIG. 19G is an example of a screen shot of a NLP engine table (STABLECLASS 2, cont'd.), according to an embodiment of the present invention.

FIG. 20A is an example of a screen shot of a NLP engine table (NEG CLASS2), according to an embodiment of the present invention.

FIG. 20B is an example of a screen shot of a NLP engine table (NEG CLASS2, cont'd.), according to an embodiment of the present invention.

FIG. 21A is an example of a screen shot of a NLP engine table(ANEURYSM), according to an embodiment of the present invention.

FIG. 21B is an example of a screen shot of a NLP engine table (ANEURYSM,cont'd.), according to an embodiment of the present invention.

FIG. 22 is an example of a screen shot of a NLP engine table (ANEURYSMALSIZE), according to an embodiment of the present invention.

FIG. 23A is an example of a screen shot of a NLP engine table (ANEURYSMLOCATIONS), according to an embodiment of the present invention.

FIG. 23B is an example of a screen shot of a NLP engine table (ANEURYSMLOCATIONS, cont'd.), according to an embodiment of the presentinvention.

FIG. 24 is an illustration of a diagram showing an example of successiveStage 3 methods applied to multiple reports resulting in multiple colorcoded rows and columns, according to an embodiment of the presentinvention.

FIG. 25 is an example of a screen shot of a larger section of a databasecontaining decoded text with secondary color coding, according to anembodiment of the present invention, noting that the actual completeddecoded database is much larger, as only a small section is visible froma given standard computer screen.

FIG. 26 is an illustration of a diagram showing an example of successiveStage 4 methods applied to a section of a decoded database, according toan embodiment of the present invention.

FIG. 27 are examples of screen shots of formula 1 and formula 2,according to an embodiment of the present invention.

FIG. 28 is an example of a screen shot displaying a variety of formularesults as displayed below a section of a decoded database, according toan embodiment of the present invention.

FIG. 29 are examples of screen shots displaying sample tabular andgraphical visual representations of the variety of formula results asdisplayed below a section of a decoded database, according to anembodiment of the present invention.

FIG. 30 are examples of tables showing how imaging protocol verificationcan be applied, according to an embodiment of the present invention.

FIG. 31 are examples of tables showing how a loss matrix and opportunityloss matrix can be applied to decision making, according to anembodiment of the present invention.

FIG. 32A is an example table showing how a loss matrix can be applied tooptimal choice decision making for test utilization (first test),according to an embodiment of the present invention.

FIG. 32B is an example table showing how a loss matrix can be applied inoptimal choice decision making for test utilization (second test),according to an embodiment of the present invention.

FIG. 32C is an example table showing how an opportunity loss matrix canbe applied to optimal choice decision making for test utilization (thirdtest), according to an embodiment of the present invention.

DETAILED DESCRIPTION

FIGS. 1 through 29 illustrate a sequential series of methods (Stages 1through 4) (140, 150, 160, 170, 175) and apparatus (Automated Analyzerclient 110 and associated server 120) for searching a text (oralphanumeric string) database, restructuring and parsing text data (oralphanumeric string), creation/application of a natural languageprocessing engine, and the creation/application of an automated analyzerto collectively achieve natural language understanding and automatedanalytics.

Referring now to FIG. 2, the global or broad perspective of standardhospital Health Level 7 (HL7) interface architecture is shown. As can beseen, the Automated Analyzer Client 110 and the natural languageprocessing (NLP) engine decoder and Automated Analyzer Server 120process text data (or alphanumeric string) results from an indexedgeneric vendor search server 100. All hospital data flows via HL7messages from individual servers, as an example PACS 150, to a centralHL7 interface engine 140, and then is retrieved and viewed from localclients, as an example an electronic medical record (EMR, or electronichealth record EHR) 130. Source servers employed during the presentinvention include PACS 150, RIS 160, ORM 170, ADT 180, Pathology 190,Cardiology 200, and Lab 210.

Referring now to FIG. 1, the process flow of the invented softwarearchitecture/methods stages can be seen. As examples, four sourcereports are contained within four individual servers (refer to FIG. 2,PACS 150, Pathology 190, EMR 130, and Lab 210), here collectivelyrepresented as a clinical database 130. The reports are transmitted byHL7 messages to a generic vendor search server 100 and then retrieved bythe Automated Analyzer Client 110. Using the Automated Analyzer Client110, the NLP engine decoder and Automated Analyzer Server 120 isinstructed to execute Stages 1 through 4 (140, 150, 160, 170, and 175)on a given database. The results of the automated analysis are thenviewed from the Automated Analyzer Client 110.

Referring now to FIG. 3, a standard imaging workflow from imaging order220 to imaging protocol 240 is shown. The imaging order 220 originatesfrom a diagnostic consideration based on clinical decision making by aphysician, nurse practitioner, or other healthcare professional. Theimaging order 220 is then received and processed by several integratedradiology servers (PACS 160, ORM 170, and ADT 180). Next, the requestedradiology imaging modality 230 appointment is requested, furtherprocessing occurs, and based on the imaging order 220 and imagingmodality 230 an initial imaging protocol 240 is planned. Each imagingprotocol 240 is designed to be specific to a modality and specific to agiven diagnostic consideration, or a very narrow range of diagnosticpossibilities.

Referring now to FIG. 4, the initial imaging protocol 240 plannedprompts further downstream workflow. First, the requested imagingmodality 230 (FIG. 3) appointment is scheduled, the appropriatetechnician(s) is alerted, and further processing occurs, such asconfirmation that the appropriate imaging modality 230 (FIG. 3) andimaging protocol 240 is planned given the initial diagnosticconsideration. Once confirmed, the examination process proceeds. First,the imaging process 250 proceeds with the use of the given modality,then the imaging protocol is performed, and then a scan is performed.Next, the Digital Imaging and Communications in Medicine (DICOM) data issent to a technologist QA workstation/gateway 260 where an image(s) isproduced. After technologist processing, the images are sent to PACS 150and RIS 160. Finally, the radiologist reading workstation 270 displaysthe resulting images for reading, finding identification, findinginterpretation, and report production. All steps in the entireexamination process are either directly performed or supervised by aradiologist (physician).

Stage 1

Referring now to FIG. 5, an example of Stage 1 methods 140 is shown asapplied to a clinical database 130. First, the clinical database 130 isindexed for keyword (free text) search within a generic vendor searchserver 100 (FIG. 2) and an optimized keyword text search (as an example,“er ct or cta dissection”) (note CTA refers to CT angiogram) is enteredinto an advanced query client 280 (refer to FIG. 6 for an enlargedversion of 280). An example screen shot of an advanced query client userinterface screen is shown. In the advanced query client, a simpleexample optimization of keyword search terms within a key-wordsearchable database is shown for basic understanding. The advanced queryclient 280 directs the optimized text search and retrieves specific andrelevant results. As examples, the results of the advanced query are oneimaging report 131 and one pathology report 132.

Referring now to FIG. 6, the results of the advanced query client 280optimized text search described in FIG. 5 are shown. A total of 2562records (reports) with hits were retrieved from a clinical databasecontaining many more reports (millions).

As an example, the detailed methods for Stage 1: optimization of keywordsearch within a keyword searchable database, using Folio Views 4.2, areas follows:

-   -   1. Use bootstrapping and jackknifing techniques (hereby        reference to the Journal of Biomedical Informatics. October        2005; 38(5):395-403 is now incorporated) on a radiologist        (physician) verified and confirmed reference standard (calendar        years 2002-2003 spreadsheet file, for aortic dissection protocol        MDCT, obtained from a standard proprietary file system (IDX)        2002 and IDX 2003, created by a laborious manual search through        paper records) to identify keyword search criteria to be used as        a starting point. The goal is to retrieve all true hits while        making an initial attempt to minimize false hits. As an example,        Folio Views 4.2 on IDX 2002 for aortic dissection protocol MDCT        using “er ct or cta dissection” as keywords. In the example of        aortic dissection protocol MDCT, this exact search query will        retrieve all records with true hits at the expense of retrieving        of records with false hits (based on confounders such as “nodal        dissection”).    -   2. Because the original file contained only a portion of        calendar year 2003 data, the analysis is focused on calendar        year 2002 only. In an attempt to recreate a new 2002 database        that matches the reference standard 2002 file in a more        efficient manner, the reference standard 2002 file is analyzed        in a successive manner for keyword search optimization. The goal        here is to maintain retrieval all of true hits while minimizing,        or eliminating, all false hits. To achieve keyword search        accuracy optimization, the following modified Boolean and power        search techniques for queries based on the imaging protocol in        question are used.        -   a. Use reference standard 2002 file in a successive manner            to repetitively test the accuracy of new keywords, as they            become identified, for a keyword based search engine (as an            example, Folio Views)        -   b. Reduce time needed to remove undesirable reports (as an            example, non-aortic dissection protocol MDCT reports)        -   c. Run keyword searchable program on database in question            (as an example, Folio Views 4.2 on IDX 2002), specifically,            when working in a windows OS→            -   Click Windows icon→open IDX 2002            -   Click Search→advanced query→type “er ct or cta                dissection” versus “er or rc ct or cta dissection or                intimal” (rapid care [RC]) (cross check results to                insure all true hits are still retrieved)→type “er or rc                ct or cta dissection or intimal or extravasation (cross                check results to insure all true hits are still                retrieved). (Folio is limited in ability, must be a term                by itself. Example: “intim” with no letters or space                behind it, although “intim/” (intim with a forward                slash) will be considered a hit because it is a                component separator or delimiter)            -   Now total search=“er or rc ct or cta dissection or                intimal or extravasation no MVA no s/pMVA no “s/p MVA”                no trauma no s/ptrauma no “s/p trauma” no “MR head” no                “MR neck” no “CT face” (Note that MVA refers to motor                vehicle accident and MR refers to magnetic resonance                imaging) (This exact search query will provide optimal                results with improved accuracy to retrieve all records                with true hits while minimizing, or eliminating.                retrieval of false hits based on confounders: this                technique eliminates “nodal dissection” hits)    -   3. After the optimal keywords are identified, the keywords are        validated against the reference standard 2002 file to insure        accuracy    -   4. After the optimal keywords are validated for the given        imaging protocol, the search is executed on a new clinical        database 130        -   a. Run keyword searchable program on database (as an            example, Folio Views 4.2 on IDX 2004), specifically, when            working in a windows OS→            -   Click Windows icon→open IDX 2004            -   Click Search→advanced query→type “er or rc ct or cta                dissection or intimal or extravasation no MVA no s/pMVA                no “s/p MVA” no trauma no s/ptrauma no “s/p trauma” no                “MR head” no “MR neck” no “CT face” (This exact search                query will provide optimal results with improved                accuracy to retrieve all records with true hits while                minimizing retrieval of false hits based on confounders:                this technique eliminates “nodal dissection” hits)            -   View→records with hits            -   Click OK            -   Click File→export data→type in file name=IDX 2004. er ct                or cta dissection→save as Rich Text File→Location saved                file on hard drive, server, etc.            -   Click OK

It is to be understood, of course, that while the exemplary methodsdetailed above are performed manually, the methods themselves can beperformed in a more automated fashion by writing a simple script orother piece of code.

Stage 2

Referring now to FIG. 7, the execution of Stage 2 methods 150 is shownas applied to a single report 131 from Stage 1 (140, refer to FIG. 5)produced results (optimized and validated keyword search retrievedimaging reports). The imaging report 131 is shown restructured andparsed into several columns of a single row within a standardspreadsheet 290 (refer to FIG. 8 for an enlarged version). A simpleexample screen shot of parsed database header terms is shown for basicunderstanding. The entire process of report restructuring and parsing isfully automated.

Referring now to FIG. 8, example column headings are shown within astandard spreadsheet 290. The imaging report 131 (refer to FIG. 7) isrestructured and parsed into text data (or alphanumeric string) cellsacross columns A to T within row 1 to match the data content describedby the column headings. As an example, in the context of aorticdissection protocol MDCT, note that column T actually represents thesummation of 23 duplicate columns, only one column T is shown forsimplicity.

Referring now to FIG. 9, a broad perspective example of successive Stage2 methods (150, refer to FIG. 7) is shown. Example screen shots ofmultiple reports are shown after restructuring and parsing into multiplerows of a single column within a standard spreadsheet database.Transition from native text, to “shrink to fit” text, to color codedcells is also shown. Starting with a clinical database 130, Stage 1(140, refer to FIG. 5) produced results are represented by sevendifferent imaging reports 131. Stage 2 methods (150, refer to FIG. 7)are then applied to the seven reports 131 and one column A containingseven rows of restructured and parsed text data (or alphanumeric string)is produced 300. As an example, column A with the header title “CTREPORT—PROCESSED” contains seven rows or cells of text data (oralphanumeric string) 300. As described by the header title, each cellcorresponds to the imaging report 131 that created it. Specifically, row1 of column A contains the processed CT report text data (oralphanumeric string) produced by Stage 2 (150, refer to FIG. 7) asapplied to imaging report 1 (131). As an example, when Stage 2 (150,refer to FIG. 7) is applied to all Stage 1 (140, refer to FIG. 5)results, each imaging report is parsed into a single row of a singlecolumn, creating a single cell (shown). Collectively, if Stage 1produced results equal 100 imaging reports, Stage 2 will produce 100cells of corresponding text data (or alphanumeric string) within ColumnA (as an example, header title “CT REPORT—PROCESSED”). In addition, datasuch as patient name, age, and each imaging report section (History,Technique, Findings, and Impression) is parsed and inserted intoadditional separate cells thereby creating additional columns of parseddata (290, refer to FIG. 8). Since a completed database may containthousands of cells with large amounts of text data (or alphanumericstring), it is more manageable to “shrink to fit” the text (300 and 310)within each cell. If the “shrink to fit” feature is enabled (300 and310), QA processing is easier during later steps and the common problemof default enabled “wrap text” is avoided. As an example, after “shrinkto fit” is applied to all cells 310, it becomes impossible to see anydata at a quick glance even though visualizing many cells in a singlewindow on a standard computer monitor is achieved. This problem isaddressed by color coding each cell containing parsed data using anautomated feature. If the cell contains data, it is highlighted orangeas a default 320. If the cell does not contain data, the backgroundcolor is unchanged and remains white 315. The color coded cells enabledownstream processing or visual programming.

Referring now to FIG. 10, successive Stage 2 methods (150, refer to FIG.7) are shown as applied to multiple reports in multiple correspondingcolor coded rows of a single column A (320) resulting in multiplecorresponding color coded columns A, B, and C (320, 330, and 340)through additional parsing and/or replication. Successive stepstransform a single column 320 of restructured and parsed report textdata (or alphanumeric string) into a large color coded database 340. Asan example, 20 imaging reports create 20 rows of cells parsed into manymore columns than labeled 340. Column parsing and/or replication can berepeated as many times as necessary to meet downstream processing oranalytic needs. A completed database can contain all aspects of data(patient demographics, report sections, full reports, etc.). As the“shrink to fit” feature remains enabled, data within each cell is notvisible to the human eye thereby improving downstream processing.

Referring now to Stage 2 in its entirety, the process creates both atranslational and non-translational database.

As an example, the detailed methods for Stage 2: Data restructuring,reformat, and parsing, using Microsoft Excel, are as follows:

-   -   The methods below describe how to manually parse Clinical        Document Architecture (CDA), Continuity of Care Record (CCR),        and Continuity of Care Document (CCD), or other standard data        formats of modern EMR systems. It is a method of Electronic Data        Interchange (EDI) parsing that allows the subsequent Stages (3        and 4) to be implemented.    -   1. Open Rich Text File in a word processing program (as an        example, Microsoft Word)→Save file using the following format        parameters (plain text, ASC II)    -   2. Click Format→reveal formatting→check distinguish style source        and show all formatting marks, deleting everything by wizard on        left    -   3. Use find/replace function to delete tabs, paragraphs, and        colons in text        -   a. Rebuild paragraphs to create rows within a spreadsheet            (setup of report A to be separated from report B) (as an            example, Microsoft Excel)            -   i. Rebuild text by separating all text with front and                back colons (flanked colons to trap desired text into                one cell within column). Depending on the origin of the                report, the delimiters may be different (as an example,                the a certain string of alphanumeric character followed                by a comma, in this case the entire string can be                searched for and replaced with a single colon to help                the later application of an automated parser).        -   b. Rebuild text a second time to create columns within a            spreadsheet (set up text report to be parsed).            -   i. The same text can be analyzed an additional time to                set delimiters (key text: as examples, examination ID                number or patient name) as the placeholders for parsing                the data into separate cells on the same row. This                allows parsing of data such as examination ID number,                patient name, patient record number, date of exam, etc.    -   4. Run spreadsheet application        -   a. Import and separate report A from report B            -   i. As an example, in Microsoft Excel→data→import                external data→import data→select                delimited→delimiter=colon→text style→finish        -   b. Import delimited text within report to be parsed within a            row into separate cells/columns            -   i. As an example, in Microsoft Excel→data→import                external data→import data→select                delimited→delimiter=pre-defined alphanumeric                strings→text style→finish            -   ii. The following additional text string examples can                serve as the “replacer text” to allow standardization of                report format                -   1. HISTORY >>                -   2. TECHNIQUE >>                -   3. FINDINGS >>                -   4. IMPRESSION >>

It is to be understood, of course, that while the exemplary methodsdetailed above are performed manually, the methods themselves can beperformed in a more automated fashion by writing a simple script orother piece of code.

Stage 3

Referring now to FIG. 11, Stage 3 methods (160) are shown as applied tomultiple reports, specifically seven reports, resulting in seven colorcoded rows of a sample two column (Column A 320 and Column B 330) colorcoded database, to simplify understanding. As representative examples,two partial NLP engine tables (341 and 346, refer to FIGS. 13A and 14Afor enlarged versions) are shown decoding or translating the text data(or alphanumeric string) within the cells or rows of both columns whilesimultaneously processing the same text data (or alphanumeric string)for application of secondary color coding. Specifically, partial NLPengine table 341 (decodes for positive AAD examinations) is applied tocolumn A (320) and partial NLP engine table 346 (decodes for Type Bexaminations) is applied to column B (330). The result of applying thepartial NLP engine tables to columns A and B are threefold. First, thetwo column database text data (or alphanumeric string) is decodedaccording to the single NLP engine table, as applied. Second, the twocolumn database undergoes secondary color coding (fill color orbackground cell color, plus or minus bolding text) according to, andmatched to, the previous text data (or alphanumeric string) decode step.Third, an iterative technique using successive steps of both text data(or alphanumeric string) decoding and application of secondary colorcoding provides a level of natural language understanding, inherentQuality Assurance, validation, and verification. In other words,decoding an examination as positive for AAD 20 times using 20 differentmarkers yields more accuracy and confidence, in the technique than asingle decoding step. In the example shown, a subtle yet important pointto realize demonstrates the iterative power of the described technique.Application of partial NLP engine table 346 (decoded for Type Bexaminations) is only valid if executed after the application of partialNLP engine table 341 (decodes for positive AAD examinations). In otherwords, the examination must be decoded as positive for AAD before thesame examination can be decoded as a subcategory (sub-classification ortype) of AAD. When applied in succession and processed together, theadditive effect of the single NLP decoding steps used in Stage 3transform the database into a form of natural language understanding.The native text data (or alphanumeric string), text data (oralphanumeric string) without a decoded result, within each cell is stillnot visible to the human eye as the “shrink to fit” feature remainsenabled thereby maintaining text data (or alphanumeric string)minimization to improve downstream processing. Further, the automatedbackground color change from no (native or white) color to orange color(row directly below header title, 320 and 330) easily demonstrates thatmany cells now contain text data (or alphanumeric string) (oralphanumeric string(s)) representing a functioning Stage 2. Further,since some of the orange cells subsequently changed from orange fillcolor to a different color (from orange to green or red), the databasealso demonstrates a functioning Stage 3.

Referring now to FIG. 12, a representative screen shot of a NLP enginetable (dissection protocol) is shown. The specific engine tabledisplayed 340 decodes the report, or alphanumeric string, for dissectionprotocol. In other words, the NLP engine decodes the report to identifyif the dissection protocol (in the context described, aortic dissectionprotocol CT is implied) was properly performed as designed. The NLPengine is deciphering if in the event the dissection protocol wasordered was it performed correctly or incorrectly. If the dissectionprotocol was performed correctly and properly as designed, the remainingdecode steps are executed. If the dissection protocol was performedincorrectly, improperly, not as designed, or was never actually ordered(220, refer to FIG. 3), then the same or a different NLP decodealgorithm can be applied. As an example, sometimes when an aorticdissection protocol CT is ordered, a different imaging protocol isactually performed for a variety of reasons, As shown, an aneurysmprotocol can be a possible anomaly or substitution and is easilyidentified by the secondary color coding applied, here the secondarycolor coding is “pink” compare to the “blue” dissection protocol). Ascan be inferred, one decoding step opens up a host of downstreamprocessing or other NLP decoding steps.

Referring now to FIG. 13A, a representative screen shot of a NLP enginetable (POSAAD) is shown. The specific engine table displayed 341 decodesthe report, or alphanumeric string, for POSAAD, or positive AADexaminations. In other words, the NLP engine decodes the report toidentify if the examination is positive for an acute aortic disorder(AAD). In the context described, aortic dissection protocol CT isimplied, and should be decoded in an antecedent manner before 341 isconsidered as a valid next step in the succession of decoding steps ordecoding algorithm. Although, note the preceding method of protocol A todiagnosis A is not a mutually exclusive algorithm or path. Decodingtable 341 can be applied in different algorithms or decoding paths asnecessary to identify, match, and assign a report as positive for AAD inthe context of an alternative diagnosis from a non-aortic dissectionspecific imaging protocol.

Referring now to FIG. 13B, a second representative screen shot of a NLPengine table (POSAAD) is shown. The specific engine table displayed 342is a continuation of 341 (refer to FIG. 13A) (the entire engine table istoo large to display on a single screen shot).

Referring now to FIG. 13C, a third representative screen shot of a NLPengine table (POSAAD) is shown. The specific engine table displayed 343is a continuation of 341 and 342 (refer to FIGS. 13A and 13B) (theentire engine table is too large to display on a single screen shot).

Referring now to FIG. 13D, a fourth representative screen shot of a NLPengine table (POSAAD) is shown. The specific engine table displayed 344is a continuation of 341, 342, and 343 (refer to FIGS. 13A, 13B, and13C) (the entire engine table is too large to display on a single screenshot).

Referring now to FIG. 13E, a fifth representative screen shot of a NLPengine table (POSAAD) is shown. The specific engine table displayed 345is a continuation of 341, 342, 343, and 344 (refer to FIGS. 13A, 13B,13C, and 13D) (the entire engine table is too large to display on asingle screen shot).

Referring now to FIG. 14A, a representative screen shot of a NLP enginetable (Type A and B) is shown (these are well described AAD subtypes inthe literature). The specific engine table displayed (346 and 347)decodes the report, or alphanumeric string, for Type A and Type B, orpositive subtypes of AAD examinations. In other words, the NLP enginedecodes the report to identify if the examination is positive for a TypeA or Type B acute aortic disorders (AAD). In the context described,aortic dissection protocol CT is implied, and should be decoded in anantecedent manner before 346 and/or 347 is considered as a valid nextstep in the succession of decoding steps or decoding algorithm.Although, the method of protocol A to diagnosis A to subtype A or B isnot always mutually exclusive algorithm or path (for the example shown,the algorithm is mutually exclusive for single finding or diagnosis).Decoding table 346 and/or 347 can be applied in different algorithms ordecoding paths as necessary to identify, match, and assign a report aspositive for a Type A or Type B AAD in the context of an alternativediagnosis from a non-aortic dissection specific imaging protocol.

Referring now to FIG. 14B, a second representative screen shot of a NLPengine table (Type B) is shown. The specific engine table displayed 348is a continuation of 347, and more specifically 346 (refer to FIG. 14A).In reality, all three engine tables should be applied dynamicallytogether as they collectively represent one large engine table. Further,Type A and Type B are mutually exclusive classifications when applied toa single finding or diagnosis (the entire engine table is too large todisplay on a single screen shot). Note that the portion of the NLPengine table displayed is for Type B only.

Referring now to FIG. 15A, a representative screen shot of a NLP enginetable (Type 1, 2, and 3) is shown (these are well described AAD subtypesin the literature). The specific engine table displayed (349) decodesthe report, or alphanumeric string, for Type 1 and Type 2, or Type 3positive subtypes of AAD examinations. In other words, the NLP enginedecodes the report to identify if the examination is positive for a Type1, Type 2, or Type 3 acute aortic disorders (AAD). In the contextdescribed, aortic dissection protocol CT is implied, and should bedecoded in an antecedent manner before 349 is considered as a valid nextstep in the succession of decoding steps or decoding algorithm. Themethod of diagnosis A to subtype 1 or 2 or 3 may or may not be amutually exclusive algorithm or path (for the example shown, thealgorithm is mutually exclusive for single finding or diagnosis).Decoding table 349 can be applied in different algorithms or decodingpaths as necessary to identify, match, and assign a report as positivefor a Type 1, Type 2, or Type 3 AAD in the context of an alternativediagnosis from a non-aortic dissection specific imaging protocol. Inaddition, the secondary color coding contains superimposed NLP orautomated understanding. The cell background color refers to adifferent, yet to be described in said application, variantclassification scheme based on the finding or diagnosis (five totalvariants, well describe in the literature for AAD). Green cell colorcorresponds to class 1 (351, refer to FIG. 16), orange cell colorcorresponds to class 2, blue cell color corresponds to class 3 (notshown), yellow cell color corresponds to class 4, and red cell colorcorresponds to class 5 (not shown). In the example of variantclassification of AAD, some are and some are not mutually exclusive,thereby making AAD variants a proper prototype for said decoding abilitydescriptions.

Referring now to FIG. 15B, a second representative screen shot of a NLPengine table (Type 3) is shown. The specific engine table displayed 350is a continuation of 349, and more specifically, Type 3 decoding withinengine table 349 (refer to FIG. 15A). In reality, the two engine tablesshould be applied dynamically together as they collectively representone large engine table. Further, Type 1, Type 2, and Type 3 are mutuallyexclusive classifications when applied to a single finding or diagnosis(the entire engine table is too large to display on a single screenshot). Note that the portion of the NLP engine table displayed is forType 3 only.

Referring now to FIG. 16, a representative screen shot of a NLP enginetable (CLASS 1) is shown (Class 1 is among five variant classes that arewell described for AAD in the literature). The specific engine tabledisplayed (351) decodes the report, or alphanumeric string, for variantClass 1 AAD examinations. In other words, the NLP engine decodes thereport to identify if the examination is positive for a variant Class 1acute aortic disorder (AAD). In the context described, aortic dissectionprotocol CT is implied, and should be decoded in an antecedent mannerbefore 351 is considered as a valid next step in the succession ofdecoding steps or decoding algorithm. The method of diagnosis A tovariant Class 1 or Class 2 or Class 3 or Class 4 or Class 5 may or maynot be a mutually exclusive algorithm or path (for the example shown,the algorithm is a not mutually exclusive for single finding ordiagnosis). Decoding table 351 can be applied in different algorithms ordecoding paths as necessary to identify, match, and assign a report aspositive for a variant Class 1, Class 2, Class 3, Class 4, or Class 5AAD in the context of an alternative diagnosis from a non-aorticdissection specific imaging protocol. In addition, the secondary colorcoding contains superimposed NLP or automated understanding. The cellbackground color refers to the specific variant classification scheme aspreviously described (351) based on the finding or diagnosis. Anadditional layer of NLP generated automated understanding can be derivedfrom the technique as shown, if more than one variant Class(es) isdecoded in a single examination, thereby representing a variablydescribed single finding versus variably described additionalfinding(s), the dominant finding (or finding with the greatest number ofdecoded hits) can be represented by the secondary color coding (asshown, CLASS 1 cells are green fill color) when applied to the variantClass column (the column with the header title Variant Class, in theexample of AAD). Note that NLP engine tables representative of theremaining variant Classes are not shown, for simplicity. (There are fivetotal variants, well described in the literature for AAD).

Referring now to FIG. 17A, a representative screen shot of a NLP enginetable (NEG CLASS 1) is shown. The specific engine table displayed (352)decodes the report, or alphanumeric string, for negative variant Class 1AAD examinations. In other words, the NLP engine decodes the report toidentify if the examination is negative for a variant Class 1 acuteaortic disorder (AAD). In the context described, aortic dissectionprotocol CT is implied, and should be decoded in an antecedent mannerbefore 352 is considered as a valid next step in the succession ofdecoding steps or decoding algorithm. The method of diagnosis A tonegative variant Class 1 or Class 2 or Class 3 or Class 4 or Class 5 mayor may not be a mutually exclusive algorithm or path (for the exampleshown, the algorithm is a not mutually exclusive for single finding ordiagnosis). Decoding table 352 can be applied in different algorithms ordecoding paths as necessary to identify, match, and assign a report aspositive for a negative variant Class 1, Class 2, Class 3, Class 4, orClass 5 AAD in the context of an alternative diagnosis, or lack thereof,from a non-aortic dissection specific imaging protocol. In addition, thesecondary color coding contains superimposed NLP or automatedunderstanding. The cell background color refers to the specific negativevariant classification scheme, similar to previously described (351,refer to FIG. 16) based on the absence of a finding or diagnosis. Anadditional layer of NLP generated automated understanding can be derivedfrom the technique as shown, if the absence of more than one variantClass(es) is decoded in a single examination, thereby representing avariably described absence of a single finding versus variably describedabsence of an additional finding(s), the dominant finding, or lackthereof (or finding with the greatest number of decoded negative hits)can be represented by the secondary color coding when applied to thenegative variant Class column (the column with the header title NegativeVariant Class, in the example of AAD). Note that NLP engine tablesrepresentative of the remaining negative variant Classes are not shown,for simplicity. (There are five total variants, well described in theliterature for AAD).

Referring now to FIG. 17B, a second representative screen shot of a NLPengine table (NEG CLASS 1) is shown. The specific engine table displayed353 is a continuation of 352 (refer to FIG. 17A). In reality, the twoengine tables should be applied dynamically together as theycollectively represent one large engine table (the entire engine tableis too large to display on a single screen shot).

Referring now to FIG. 17C, a third representative screen shot of a NLPengine table (NEG CLASS 1) is shown. The specific engine table displayed354 is a continuation of 352 and 353 (refer to FIGS. 17A and 17B). Inreality, the three engine tables should be applied dynamically togetheras they collectively represent one large engine table (the entire enginetable is too large to display on a single screen shot).

Referring now to FIG. 18, a representative screen shot of a NLP enginetable (CLASS 2) is shown (Class 2 is among five variant classes that arewell described for AAD in the literature). The specific engine tabledisplayed (355) decodes the report, or alphanumeric string, for variantClass 2 AAD examinations. In other words, the NLP engine decodes thereport to identify if the examination is positive for a variant Class 2acute aortic disorder (AAD). In the context described, aortic dissectionprotocol CT is implied, and should be decoded in an antecedent mannerbefore 355 is considered as a valid next step in the succession ofdecoding steps or decoding algorithm. The method of diagnosis A tovariant Class 1 or Class 2 or Class 3 or Class 4 or Class 5 may or maynot be a mutually exclusive algorithm or path (for the example shown,the algorithm is a not mutually exclusive for single finding ordiagnosis). Decoding table 355 can be applied in different algorithms ordecoding paths as necessary to identify, match, and assign a report aspositive for a variant Class 1, Class 2, Class 3, Class 4, or Class 5AAD in the context of an alternative diagnosis from a non-aorticdissection specific imaging protocol. In addition, the secondary colorcoding contains superimposed NLP or automated understanding. The cellbackground color refers to the specific variant classification scheme aspreviously described (351, refer to FIG. 16) based on the finding ordiagnosis. An additional layer of NLP generated automated understandingcan be derived from the technique as shown, if more than one variantClass(es) is decoded in a single examination, thereby representing avariably described single finding versus variably described additionalfinding(s), the dominant finding (or finding with the greatest number ofdecoded hits) can be represented by the secondary color coding (asshown, CLASS 2 cells are red fill color) when applied to the variantClass column (the column with the header title Variant Class, in theexample of AAD). Note that NLP engine tables representative of theremaining variant Classes are not shown, for simplicity. (There are fivetotal variants, well described in the literature for AAD).

Referring now to FIG. 19A, a representative screen shot of a NLP enginetable (STABLE CLASS 2) is shown. The specific engine table displayed(356) decodes the report, or alphanumeric string, for stable variantClass 2 AAD examinations. In other words, the NLP engine decodes thereport to identify if the examination is stable for a variant Class 2acute aortic disorder (AAD). In the context described, aortic dissectionprotocol CT is implied, and should be decoded in an antecedent mannerbefore 356 is considered as a valid next step in the succession ofdecoding steps or decoding algorithm. The method of diagnosis A tostable variant Class 1 or Class 2 or Class 3 or Class 4 or Class 5 mayor may not be a mutually exclusive algorithm or path (for the exampleshown, the algorithm is a not mutually exclusive for single finding ordiagnosis). Decoding table 356 can be applied in different algorithms ordecoding paths as necessary to identify, match, and assign a report aspositive for a stable variant Class 1, Class 2, Class 3, Class 4, orClass 5 AAD in the context of an alternative diagnosis, or lack thereof,from a non-aortic dissection specific imaging protocol. In addition, thesecondary color coding contains superimposed NLP or automatedunderstanding. The cell background color refers to the specific stablevariant classification scheme, similar to previously described (351,refer to FIG. 16) based on the finding or diagnosis. An additional layerof NLP generated automated understanding can be derived from thetechnique as shown, if more than one stable variant Class(es) is decodedin a single examination, thereby representing a variably describedpresence/absence of a single finding versus variably describedpresence/absence of an additional finding(s), the dominant finding, orlack thereof (or finding with the greatest number of decoded hits) canbe represented by the secondary color coding when applied to the stablevariant Class column (the column with the header title Stable VariantClass, in the example of AAD). Note that NLP engine tablesrepresentative of the remaining stable variant Classes are not shown,for simplicity. (There are five total variants, well described in theliterature for AAD).

Referring now to FIG. 19B, a second representative screen shot of a NLPengine table (STABLE CLASS 2) is shown. The specific engine tabledisplayed 357 is a continuation of 356 (refer to FIG. 19A). In reality,the two engine tables should be applied dynamically together as theycollectively represent one large engine table (the entire engine tableis too large to display on a single screen shot).

Referring now to FIG. 19C, a third representative screen shot of a NLPengine table (STABLE CLASS 2) is shown. The specific engine tabledisplayed 358 is a continuation of 356 and 357 (refer to FIGS. 19A and19B). In reality, the three engine tables should be applied dynamicallytogether as they collectively represent one large engine table (theentire engine table is too large to display on a single screen shot).

Referring now to FIG. 19D, a fourth representative screen shot of a NLPengine table (STABLE CLASS 2) is shown. The specific engine tabledisplayed 359 is a continuation of 356, 357, and 358 (refer to FIGS.19A, 19B, and 19C). In reality, the four engine tables should be applieddynamically together as they collectively represent one large enginetable (the entire engine table is too large to display on a singlescreen shot).

Referring now to FIG. 19E, a fifth representative screen shot of a NLPengine table (STABLE CLASS 2) is shown. The specific engine tabledisplayed 360 is a continuation of 356 through 359 (refer to FIG. 19Athrough 19D). In reality, the five engine tables should be applieddynamically together as they collectively represent one large enginetable (the entire engine table is too large to display on a singlescreen shot).

Referring now to FIG. 19F, a sixth representative screen shot of a NLPengine table (STABLE CLASS 2) is shown. The specific engine tabledisplayed 361 is a continuation of 356 through 360 (refer to FIG. 19Athrough 19E). In reality, the six engine tables should be applieddynamically together as they collectively represent one large enginetable (the entire engine table is too large to display on a singlescreen shot).

Referring now to FIG. 19G, a seventh representative screen shot of a NLPengine table (STABLE CLASS 2) is shown. The specific engine tabledisplayed 362 is a continuation of 356 through 361 (refer to FIG. 19Athrough 19F). In reality, the seven engine tables should be applieddynamically together as they collectively represent one large enginetable (the entire engine table is too large to display on a singlescreen shot).

Referring now to FIG. 20A, a representative screen shot of a NLP enginetable (NEG CLASS 2) is shown. The specific engine table displayed (363)decodes the report, or alphanumeric string, for negative variant Class 2AAD examinations. In other words, the NLP engine decodes the report toidentify if the examination is negative for a variant Class 2 acuteaortic disorder (AAD). In the context described, aortic dissectionprotocol CT is implied, and should be decoded in an antecedent mannerbefore 363 is considered as a valid next step in the succession ofdecoding steps or decoding algorithm. The method of diagnosis A tonegative variant Class 1 or Class 2 or Class 3 or Class 4 or Class 5 mayor may not be a mutually exclusive algorithm or path (for the exampleshown, the algorithm is a not mutually exclusive for single finding ordiagnosis). Decoding table 363 can be applied in different algorithms ordecoding paths as necessary to identify, match, and assign a report aspositive for a negative variant Class 1, Class 2, Class 3, Class 4, orClass 5 AAD in the context of an alternative diagnosis, or lack thereof,from a non-aortic dissection specific imaging protocol. In addition, thesecondary color coding contains superimposed NLP or automatedunderstanding. The cell background color refers to the specific negativevariant classification scheme, similar to previously described (351,refer to FIG. 16) based on the absence of a finding or diagnosis. Anadditional layer of NLP generated automated understanding can be derivedfrom the technique as shown, if the absence of more than one variantClass(es) is decoded in a single examination, thereby representing avariably described absence of a single finding versus variably describedabsence of an additional finding(s), the dominant finding, or lackthereof (or finding with the greatest number of decoded negative hits)can be represented by the secondary color coding when applied to thenegative variant Class column (the column with the header title NegativeVariant Class, in the example of AAD). Note that NLP engine tablesrepresentative of the remaining negative variant Classes are not shown,for simplicity. (There are five total variants, well described in theliterature for AAD).

Referring now to FIG. 20B, a second representative screen shot of a NLPengine table (NEG CLASS 2) is shown. The specific engine table displayed364 is a continuation of 363 (refer to FIG. 20A). In reality, the twoengine tables should be applied dynamically together as theycollectively represent one large engine table (the entire engine tableis too large to display on a single screen shot).

Referring now to FIG. 21A, a representative screen shot of a NLP enginetable (ANEURYSM) is shown. The specific engine table displayed 365decodes the report, or alphanumeric string, for ANEURYSM, or positiveaneurysm examinations. In other words, the NLP engine decodes the reportto identify if the examination is positive for an aneurysm, one of theacute aortic disorders (AAD). In the context described, aorticdissection protocol CT is not necessarily implied, and does not need tobe decoded in an antecedent manner before 365 is considered as a validnext step in the succession of decoding steps or decoding algorithm. Thepreceding method of protocol A to diagnosis A is a mutually exclusivealgorithm or path (in the context of aortic dissection protocol CT).Decoding table 365 can be applied in different algorithms or decodingpaths as necessary to identify, match, and assign a report as positivefor an aneurysm in the context of an alternative diagnosis from anon-aortic dissection specific imaging protocol.

Referring now to FIG. 21B, a second representative screen shot of a NLPengine table (ANEURYSM) is shown. The specific engine table displayed366 is a continuation of 365 (refer to FIG. 21A) (the entire enginetable is too large to display on a single screen shot).

Referring now to FIG. 22, a representative screen shot of a NLP enginetable (ANEURYSMAL SIZE) is shown. The specific engine table displayed367 decodes the report, or alphanumeric string, for ANEURYSMAL SIZE, orpositive aneurysm examinations based on a described alphanumerical size.In other words, the NLP engine decodes the report to identify if theexamination is positive for an aneurysm, one of the acute aorticdisorders (AAD), based on an actual number or measurement. In thecontext described, aortic dissection protocol CT is not necessarilyimplied, but does not need to be decoded in an antecedent manner before367 is considered as a valid next step in the succession of decodingsteps or decoding algorithm. However, in the context described, anexamination decoded positive for an aneurysm needs to occur antecedentor subsequent for 367 to be considered a valid decoding step. Thepreceding method of size (measurement or numerical value) A to diagnosis(or finding) A is a not mutually exclusive algorithm or path (in thecontext of aneurysmal size within aortic dissection protocol CT).Decoding table 367 can be applied in different algorithms or decodingpaths as necessary to identify, match, and assign a report as positivefor an aneurysmal size in the context of an alternative diagnosis from anon-aortic dissection specific imaging protocol. Further, and moreimportantly, the method of decoding size (measurement or numericalvalue) to aneurysmal size is not the only option using the describedtechnique. The decoding table 367, or a similar decoding table, can beredefined, verified, and validated for different diagnoses and/orfindings given the proper antecedent (or subsequent) decoding steps asrequired to provide the necessary diagnostic category (classification)or relevance (as examples, size or measurement of a lung nodule ordisplacement of a bone fracture).

Referring now to FIG. 23A, a representative screen shot of a NLP enginetable (ANEURYSM LOCATIONS) is shown (aneurysmal location categories, andvascular dilatations not meeting aneurysmal size, are well described forAAD in the literature). The engine table categorizes newly diagnosedaneurysms into following: ascending thoracic aorta, descending thoracicaorta, and abdominal aorta. The engine table also recognizes aorticdilatation locations not meeting aneurysmal size. The specific enginetable displayed (368) decodes the report, or alphanumeric string, fornewly diagnosed aneurysmal (or vascular dilatation) locations (one ofthe categories of positive AAD examinations). Specifically, the decodertable defines locations according to the following locations: (NEW ASCTAA, NEW DES TAA, NEW AAA, NEW ASC DIL, and NEW DES DIL), where AAArefers to Abdominal Aortic Aneurysm. In other words, the NLP enginedecodes the report to identify if the examination is positive for a newaneurysmal (or vascular dilatation) location acute aortic disorder(AAD). The engine table categorizes newly diagnosed aneurysms (orvascular dilatation) into following: ascending thoracic aorta,descending thoracic aorta, and abdominal aorta. The engine table alsorecognizes aortic dilatation locations not meeting aneurysmal size.Further, when decoding table 368 is applied in combination with decodingtables such as 366 and 367, decoding table 368 now generates andprovides artificial and automated report understanding for a finding(s)location within the human body based on the given examination. In thecontext described, aortic dissection protocol CT is implied, but doesnot be decoded in an antecedent manner before 368 is considered as avalid next step in the succession of decoding steps or decodingalgorithm. The method of diagnosis A to category A, B, C, D, or E, etc.,may or may not be a mutually exclusive algorithm or path (for theexample shown, the algorithm is a not mutually exclusive for singlefinding or diagnosis). Note that because the method, or algorithm, shownand described in not mutually exclusive for each category (NEW ASC TAA,NEW DES TAA, NEW AAA, NEW ASC DIL, and NEW DES DIL), each category needsto be reassigned into separate NLP engine decoding tables for the methodto execute as designed. Decoding table 368 is shown, as an example, ofwhat is considered an incorrectly designed decoding table in the contextof an algorithm for categories that are not mutually exclusive.(Decoding table 368 can be applied in different algorithms or decodingpaths as necessary to identify, match, and assign a report as positivefor a new aneurysmal (or vascular dilatation) location AAD in thecontext of an alternative diagnosis from a non-aortic dissectionspecific imaging protocol. In addition, the secondary color codingcontains superimposed NLP or automated understanding. The cellbackground color, as shown in decoder table 368, refers to theidentification and sub-classification based on two categories: aneurysm(blue cell fill color) versus vascular dilatation (yellow cell fillcolor) based on the finding or diagnosis. As examples, also representedare the use single character wildcards, here in place of numericalvalues, as identified during the decoding step for aneurysmal (orvascular dilatation) location. Numerical values can also be identifiedas how far away, in single character increments within an alphanumericstring in a cell, they are from a keyword such aneurysm (or positiveAAD) to denote level of confidence in the identified value beingassociated with said keyword. An additional layer of NLP generatedautomated understanding can be derived from the technique as shown, ifmore than one sub-classification is decoded in a single examination,thereby representing a variably described single finding versus variablydescribed additional finding(s), the dominant finding (or finding withthe greatest number of decoded hits) can be represented by the secondarycolor coding (as shown, NEW DES TAA cells are blue fill color) whenapplied to the aneurysm location column (the column with the headertitle Aneurysm Location, in the example of AAD).

Referring now to FIG. 23B, a second representative screen shot of a NLPengine table (ANEURYSM LOCATIONS) is shown (note that thesubclassifcation for NEW DES DIL in not actually represented). Theengine table categorizes newly diagnosed aneurysms into following:ascending thoracic aorta, descending thoracic aorta, and abdominalaorta. The engine table also recognizes aortic dilatation locations notmeeting aneurysmal size. The specific engine table displayed 369 is acontinuation of 368 (refer to FIG. 23A). In reality, the two enginetables should be applied dynamically together as they collectivelyrepresent one large engine table (the entire engine table is too largeto display on a single screen shot).

Referring now to FIG. 24, two minimized representative screen shots oftwo partial NLP engine tables (341, 346, 347, refer to FIGS. 13A and 14Afor enlarged versions) as applied to a two column database correspondingto seven reports 370 (also refer to FIG. 11), are shown. The two columndatabase, shown for simplicity and ease of understanding, represents asmall section of a much larger database 380. Successive Stage 3 methodsare applied to multiple reports resulting in multiple color coded rowsand columns. As an example, four partial NLP engine tables are shown(341, 346, 347, 349, and 351, refer to FIGS. 13A, 14A, 15A, and 16 forenlarged versions) processing a larger representative section of thedatabase 380 into decoded text with secondary color coding. As morecolumns containing parsed text are added to the database, more decodingsteps can be applied. The result is a large database containing decodedtext with secondary color coding 380 (refer to FIG. 25 for an enlargedversion). Notice that other decoding steps, not shown, were processed tobuild the right aspect of the larger section of the database 380 (cellswith blue, yellow, red fill color). The color coded cells enabledownstream processing or visual programming. (Note that native text data(or alphanumeric string), text data (or alphanumeric string) without adecoded result, within each cell is not visible to the human eye becausethe text data (or alphanumeric string) remains minimized to improvedownstream processing).

Referring now to FIG. 25, a representative screen shot of a largersection of the decoded database 381 is shown. The section of decodeddatabase 381 is shown containing several different columns, of manydifferent reports or rows, of decoded text with secondary color coding.The actual completed decoded database is much larger. Even thoughdecoded database 381 is enlarged compared to decoded database 380 (referto FIG. 24), only a small section is visible from a given standardcomputer screen. Note that the exact sample section shown in decodeddatabase 381 is of a different representative section than shown indecoded database 381 (refer to FIG. 24). The representative section ofdecoded database 381 is intentionally from a different section of thesame decoded database as 380 to better illustrate the decoded text cellswith secondary color coding, the larger text size is now readable. Thechange in color against the background orange color allows efficientidentification of a working NLP engine as decoded text with secondarycolor coding. Alternatively, decoded text without the proper secondarycolor coding is difficult to identify efficiently illustrating theimportance of secondary color coding. As an example, decoded textwithout proper secondary color coding is shown as “Type 3” with orangecolor coding against an orange color coded background (382). Withreference and acclimation to 340, 380, and 381 (refer to FIGS. 10, 24,and 25) the reader can now appreciate the difference between NLP enginetext decoding yields if 340 is considered as “no yield” (in reality theNLP engine was not applied to 340, if it was applied then the QAengineer should consider the possibility that the NLP engine was notexecuting properly), 380 is considered as “low yield,” and 381 isconsidered as “high yield.” The differences in NLP engine text decodingyields are only efficiently identified by the human eye by focusing onthe secondary color coding. The color coded cells enable this method ofhuman processing, or visual programming, during QA analysis or othermanual analysis. (Note that native text data (or alphanumeric string),text data (or alphanumeric string) without a decoded result, within eachcell is still not visible to the human eye because the text data (oralphanumeric string) remains minimized to improve downstreamprocessing).

Referring now to FIGS. 11 through 25, or Stage 3 methods collectively,the natural tendency for radiology reporting (or clinical reporting ingeneral) is not to identify a protocol/report as positive or negativefor disease, instead the prose is intentionally somewhere in the middleof the spectrum for a variety of reasons (legal, knowledge base,certainty, examination quality, goal of examination, etc.). This createsdifficulty for traditional NLP text decoding algorithms as the text tobe decoded is not simply a binary result of positive or negative as inother forms of human language and medical information. The methods asdescribed in FIGS. 11 through 23B address the problems not solved byother more traditional NLP methods or techniques.

As an example, the detailed steps for Stage 3: Natural languageprocessor (NLP) engine (text decoder or translator) as applied to theradiology or medical language, are as follows:

-   -   1. Create a reference standard for comparison by manually        searching previously verified and validated imaging reports to        find key terms (these terms serve as the “find” phrases in the        generic batch find and replace program). As an example,        reference to the aortic dissection protocol MDCT database        comprised of positive, negative, and indeterminate imaging        reports derived from IDX 2002 (373 examinations) and IDX 2004        (475 examinations), previously verified and validated by a        manual process, is hereby incorporated in subsequent steps as        the reference standard.    -   2. Using the key terms identified in Step 1, create decoder        tables (translation tables) as appropriate (as examples, decoder        tables for positive, negative, and Type A aortic dissection        protocol CT examinations).    -   3. Apply wildcard flanks to all key terms in the search column        of each decoder table as follows (347, refer to FIG. 14A):        -   a. As an example, the find term “ascending arch to the            aortic bifurcation” appears as follows: “*ascending arch to            the aortic bifurcation*” in row 1 in column A        -   b. As an example, the replace term “Type A” appears as            follows: “Type A” with any formatting specified (as an            example, yellow cell fill color) in row 1 in column B    -   4. Starting from the end product (result) from Stage 2, the text        (alphanumeric string) database containing parsed data, with the        last column containing the processed reports to be        decoded/translated, copy entire the column:        -   a. As an example, consider the given spreadsheet database            file name: aortic dissection protocol MDCT.xls containing            column T in 290 (refer to FIG. 8), here column T contains            one row of sample text data in row 2 (a single cell with a            single Stage 2 processed report)        -   b. Select column T data        -   c. Copy column T data (as an example, Ctrl+C)        -   d. Close said spreadsheet file (as an example, aortic            dissection protocol MDCT.xls)    -   5. Open a new blank spreadsheet (as an example, temporary        file.xls) and then paste copied column T from Step 4c into the        said spreadsheet. Save file. Close file.    -   6. Download and install a generic batch find and replace        program. As an example, Useful File Utilities Batch Replacer        (UFU) for Microsoft (MS) Excel is hereby incorporated as        reference as the generic batch find and replace program, all        subsequent steps are specific to UFU execution (instructions        available through UFU are hereby incorporated as reference for        further detail).    -   7. Open generic batch find and replace program (UFU)    -   8. Follow generic batch find and replace program instructions        and replace file with alphanumeric strings placed in decoder        table (translation table), specifically:        -   a. Enable the following options/settings:            -   i. Browse to and then right click given file                (spreadsheet or database, as an example, temporary                file.xls from Step 6) to be decoded so that the file is                highlighted red            -   ii. Send selected file to basket (may or may not need                this option enabled depending on program version)            -   iii. Select “batch replacer for MS Excel”            -   iv. Browse to appropriate translation table            -   v. Select the following parameters: “whole cell” and                search in “values”            -   vi. Click “Advanced Options”            -   vii. Check all appropriate options under “formatting for                the replace with text” and set “Where to make changes”                to “make changes in the original file and create backup                file” or “save changes to a new file whose name is                generated by the Mask:” and input the appropriate file                name into the Mask: C:\desktop\temporary file.xls, and                then Click “ok”            -   viii. Click “Start Replace”.    -   9. Reopen decoded spreadsheet file (as an example, resulting        temporary file.xls from Step 8 sub-step vii) and then copy the        decoded column (as an example, the decoded column is now located        in column A), specifically:        -   a. Select column A and then copy (Ctrl+C) within temporary            file.xls    -   10. Reopen database file (as an example, aortic dissection        protocol CT.xls from Step 4a) and then paste the decoded column        A into column U in reference to 290 (refer to FIG. 8), or the        next empty column to the right. Column U is now considered an        NLP engine processed column compared to raw (non-decoded, but        Stage 2 processed) text data in column T. Save updated database        file.    -   11. Repeat Steps 4 and 5. There should now be a fresh copy of        the processed reports in Column A to be decoded (translated)        within a new file (as an example, temporary file. version2.xls).    -   12. Repeat Steps 7 through 10 while applying a different NLP        engine decoder table as appropriate.    -   13. Repeat above steps as necessary to fulfill the number of        desired decoding (translation steps). The end result is a        completed database containing an unlimited number of decoded        (translated) report columns that are ready for analysis using        the Automated Analyzer, as described in Stage 4. In other words,        in a repetitive and iterative fashion apply the created decoder        tables (NLP engine tables) from Steps 1-3 by using a generic        batch find and replace program to as applied to a text data (or        alphanumeric string) database created by Stages 1 through 2. As        an example, for aortic dissection protocol MDCT, 23 distinct        decoder tables were created (refer to the detailed description        of FIGS. 8, and 340 through 369, refer to FIGS. 12 through 23B).        The 23 distinct decoder tables represent 23 separate NLP        decoding steps containing a total of 4821 iterative phrases to        decode (translate) a given report with near 100% accuracy. The        result is a completely decoded report that is automatically        categorized into 23 separate categories (as an example, the        category in question can be disease X, disease sub-category X,        disease location X, disease timing X, disease size X, etc.)        depending on the report findings. (Note the application of        decoder tables using a generic batch find and replace program        can be applied to either Stage 2 processed (restructured and        parsed) reports containing delimiters or to Stage 2 unprocessed        reports (thereby bypassing Stage 2) without delimiters; however,        the capability of Stage 3 and 4 decreases when Stage 2        processing is not applied).        -   a. As an example, first apply the negative decoder table,            then apply the positive decoder table, and then apply the            Type A decoder table, etc.    -   14. Verify the steps performed properly by visually identifying        the secondary color coding changes, as previously described.

As an example, further detailed steps for Stage 3: Natural languageprocessor (NLP) engine (text decoder or translator) as applied to theradiology or medical language, are as follows:

-   -   1. To increase the accuracy of the NLP engine        (decoding/translation process) successive sub-steps are used to        find the phrase in question and replace the “word”, rather than        the “cell”, in an iterative fashion. (As an example, using UFU        parlance setting the parameters to “whole word” rather than        “whole cell”). The resulting cell (containing the multi-step        decoded report) may have several “find phrases” corresponding to        “replaced phrases.” The summation of all of these “replaced        phrases” within the single cell can be split in a binary        fashion, as follows:        -   a. If positive (as an example, the “replaced phrase”            corresponds to positive for aortic dissection) the “replaced            phrase” can be set to equal 1 (counted as 1 value).        -   b. If negative (as an example, the “replaced phrase”            corresponds to negative for aortic dissection) the “replaced            phrase” can be set to equal 0 (counted as 1 value).        -   c. The sum total of all “replaced phrases” can be calculated            and divided by the total number of values (“1” or “0”            replaced phrases) to yield a decimal (or fraction).        -   d. The resulting decimal (or fraction) can be considered how            positive or negative the overall report is for the finding            category in question (as an example, the category in            question can be disease X, disease sub-category X, disease            location X, disease timing X, disease size X, etc.)            -   i. 1.0=positive            -   ii. 0.7=Likely positive            -   iii. 0.5=indeterminate            -   iv. 0.3=likely negative            -   v. 0=negative        -   e. The incorporation of these sub-steps to decode            (translate) each finding category allows the approximation            of actual meaning within a text report. This sub-step method            allows decoding (translation) of reports even when the text            contradicts itself in different areas within the same report            (in instances of inaccurate voice transcription or            typographical errors). The method also allows decoding            (translation) of text when the report is intentionally vague            in cases of truly questionable or indeterminate findings            (typically referred to “hedging” by a radiologist).    -   2. To increase the accuracy of the NLP engine        (decoding/translation process) successive sub-steps are used in        an alternative approach compared to the concept described in        Step 1 (refer to further detailed steps for Stage 3). Using an        alternative approach, accuracy can be further improved by        applying the sub-steps as described to the “whole cell” rather        than the “whole word” (refer to the detailed description of FIG.        11). Using these parameters allows application of the previously        described secondary color coding to be utilized. The downside to        altering the parameters to “whole cell” when performing accuracy        improvement is that a new (fresh, non-decoded) source text data        column is required for each successive decoding step, and more        importantly, only a single batch find phrase and replace phrase        can be used at each decoding step.    -   3. To increase the accuracy of the NLP engine, a different step        involving the counting of characters representing numerical        values and counting how far away in terms of single character        spaces they are from keywords can be applied, specifically:        -   a. As an example, the numerical value “5” is seven            characters away from the keyword “aneurysm” (or 9 spaces and            characters away) in the following alphanumeric string (“5 cm            sized aneurysm”).    -   4. As an example, certain data is required in order to create        billing code decoder tables. Typical billing codes are in        reference to examination details defined in ADT data or imaging        report headings (as an example, with or without contrast). When        provided, the billing code can be used as source data to create        standard “replace” phrases when the defined ADT “find” phrases        are identified.    -   5. As an example, the capability and accuracy of the NLP engine        can be verified and validated using aortic dissection protocol        MDCT and the following steps:        -   a. Using the reference standard aortic dissection protocol            MDCT positive, negative, and indeterminate imaging reports            derived from IDX 2002 (373 examinations) and IDX 2004 (475            examinations), previously verified and validated by a manual            process, all 23 NLP decoding tables were applied to Stages 1            through 2 processed imaging reports from IDX 2002 and IDX            2004, as previously described in detail. The NLP engine            results were highly accurate.    -   6. As an example, the broad and generalized capability and        accuracy of the NLP engine can applied to renal stone protocol        MDCT using the following steps:        -   a. Create a reference standard for comparison by manually            searching previously verified and validated imaging reports            to find key terms (these terms serve as the “find” phrases            in the generic batch find and replace program). As an            example, renal stone protocol MDCT positive, negative, and            indeterminate imaging reports derived from calendar year            2011 (257 examinations) and calendar year 2012 (300            examinations), previously verified and validated by a manual            process, is hereby incorporated in subsequent steps as the            reference standard.        -   b. Using the reference standard from Step “a” containing            renal stone protocol MDCT positive, negative, and            indeterminate imaging reports, 10 NLP decoding tables were            applied, using Steps 2 through 15 (refer to the detailed            Steps for Stage 3), to Stages 1 through 2 processed imaging            reports from calendar year 2011 (257 examinations) and            calendar year 2012 (300 examinations) for renal stone            protocol MDCT, while taking into account the appropriate            alterations (the given protocol is renal stone protocol MDCT            rather than aortic dissection protocol MDCT), as previously            described in detail. The NLP engine results were highly            accurate.    -   7. As examples, other categories, besides AAD classifications        from positive aortic dissection protocol MDCT examinations or        positive, negative, and indeterminate renal stone protocol MDCT        examinations, that can be decoded are as follows:        -   a. Demographics (DEMO) from ADT data within several reports            types.        -   b. Vital Signs (V/S) from clinical reports—an example of            automated Blood Pressure (BP) analysis.        -   c. Signs and Symptoms (Si/Sx) from clinical reports—an            example of a variety of pain locations, and signs such as            unequal BP in the upper extremities, with automated            analysis.        -   d. PMH from clinical reports—examples of automated analysis            on medical syndromes such as Marfan's and Ehler Danlos, as            well as a variety of surgical aortic repairs and valve            repairs.        -   e. Electrocardiogram (EKG) reports—example of automated            analysis of associated EKGs. This also demonstrates the NLP            engine decoder can be applied to non-imaging tests to            extract understanding.        -   f. Chest x-ray imaging reports (CXR)—Another example of the            NLP engine decoder as applied on a different radiology            examination.        -   g. Surgical notes and reports.

It is to be understood, of course, that while the exemplary methodsdetailed above are performed manually, the methods themselves can beperformed in a more automated fashion by writing a simple script orother piece of code.

Stage 4

Referring now to FIG. 26, a comprehensive diagram with representativeexamples of successive Stage 4 methods (170) as applied to a section ofa decoded database 390, is shown. The section of decoded database 390shown is actually the same database section in 380 (refer to FIG. 24)with the addition of several example formulas displayed surrounding thedatabase 400 and 410 (refer to FIG. 27 for enlarged versions). Severalexample formula results are also shown below database 390 (refer to FIG.28 for an enlarged version), as applied to the entire decoded database(too large to practically illustrate). As an example (400), an entirecolumn of decoded text within the database is fed into formula 1 (1A),analyzed by formula 1 (1B), and then the result is displayed (1C). As anadditional example (410), an entire column of decoded text within thedatabase is fed into formula 2 (2A), analyzed by formula 2 (2B), andthen the result is displayed (2C). As sample portions of additionalexamples, the result from formula 3 analyzing a different decoded columnis displayed (3C), the result from formula 4 analyzing yet anotherdecoded column is displayed (4A, 4B, to 4C), and so on and so forth. Asmore columns containing parsed text are added to the database, and moredecoding steps are applied, more advanced automated analytics can alsobe applied. The result is a large decoded database with a variety ofautomated analytics 390 (refer to FIG. 28 for an enlarged version).(Note that native text data (or alphanumeric string), text data (oralphanumeric string) without a decoded result, within each cell is notvisible to the human eye because the text data (or alphanumeric string)remains minimized to improve downstream processing).

Referring now to FIG. 27, two representative screen shots of two exampleformulas, formula 1 (400) and formula 2 (410), are shown. Formula 1 andformula 2 are outlined and diagramed while analyzing the decoded textwithin the database. Specifically, formula 1 (400) analyzes decoded textfor the positivity rate of AAD and displays the result as a percentage.Further, formula 2 (410) analyzes decoded text for examinations negativefor AAD and displays the result as a number.

Referring now to FIG. 28, a representative screen shot displaying avariety of formula results below a section of a decoded database (390),is shown. Specifically, formula 4 (4A, 4B, & 4C, refer to FIG. 26) isenlarged and outlined to display the flow of Stage 4 analyticprocessing: NLP decoded text data (or alphanumeric string) is deliveredto the formula, the data is analyzed, and then the result is displayed.

Referring now to FIG. 29, several representative screen shots displayingsample tabular and graphical visual representations (175) of the varietyof Stage 4 formula results as displayed below a section of a decodeddatabase (390), are shown. As examples, the result from formula 4 (referto FIGS. 26 and 28) and the results from related formulas (395) can bevisually displayed as a color table, a color bar graph with percentages,a color combination bar/line graph with percentages and numbers, a colorpie chart, or a color bar graph with only numbers. The visualrepresentation of automated Stage 4 analytics enables additionalunderstanding of the results.

Referring now to FIG. 30, several representative tables showing 2×2contingency tables for Multi-Detector CT (MDCT) imaging protocolvalidation (450 and 460), are shown. Specifically, positive,indeterminate, and negative MDCT examination are shown in a 2×2 matrix(450) with corresponding odds, likelihood ratios, and probabilities(460). As an example, three “Judgment Calls” (refer to FIG. 30) areshown in progress: (1) Assign a pre-test probability: Should you image apatient using the Aortic Dissection Protocol MDCT if your pre-testprobability is 25%? (2) Set cut-off: set Intermediate and Positive MDCTtests as the cut-off (consider my patient as a positive test if found tobe in one of these categories). (3) Set threshold for decision change:Threshold will be set at 90%, because surgery is not benign. Now, usingBayes' Theorem: the Pre-test probability is 25%=0.25, Pre-testodds=p/1−p=0.25/1−0.25=0.33, Post-test odds=pre-test odds×LR. Also setLR=Likelihood ratio derived from validation data given our cut-off whereLR=a/a+c/b/b+d=disease/no disease=sensitivity/1−specificity. Nowcalculate the LR for all test result categories (refer to FIG. 30).Using these LR's you can calculate the resulting post-test odds ofdisease where Post-test probability=posttest odds/1+posttest odds. Nowinterpret the results. Since we considered either an Indeterminate orPositive MDCT as a Positive Case we will add the post-test probabilitiesof the two. 0.99+0=0.99=99%. Since our cut-off for outcome change was90%, this result is important. The important point is, using our threeJudgment Calls and Validation Data for MDCT, that the post-testprobability of Positive Disease given our criteria positive (Pos) orindeterminate (Indeterm) for a test result is 99%. Since our firstJudgment Call was a pre-test probability of 25%, and our third Judgmentcall for treatment was a cut-off of 90%, the fact that a proper resulton the test can boost this probability of 25% to 99% is significant.This ability of a Positive or Indeterminate test result on MDCT tochange our treatment plan provides sufficient evidence to warrant theuse of MDCT imaging protocol in question. In other words, the usualprocess of diagnosis involves data retrieval, assigning of relativeimportance to each data point, and then deriving a differentialdiagnosis. Each diagnosis in the differential diagnosis list is excludedone by one as assessment using experience based diagnostic “feelings”and/or “impressions” regarding a patient's evaluation is processed(subjective data).

Referring now to FIG. 31, several representative tables for treatmentdecision making analysis according to a loss matrix (470 and 480) and anopportunity loss matrix (490), are shown. Progression of logic is shownas flowing from 470, to 480, and to 490. As an example, the totalprobability in population P(A)=0.95 and P(B)=0.05 with a Patient Powerindifferent up to $1000 loss is displayed in 470 and 480. The use of anopportunity loss matrix (490) in decision making helps withidentification of AVOIDABLE losses that do not change the optimaldecision. According to the analysis in 490, the optimal choice for CauseA is no treatment (No Tx), but for Cause B is treatment (Tx), whenopportunity cost is factored into the decision making process.

Referring now to FIG. 32A, a representative table for test utilizationin decision making analysis according to factoring in probabilityassessment as applied to a loss matrix (500), is shown. Progression oflogic is shown flowing in 500. In the example of the first test (500),given the probabilities shown, the cheaper decision for a positive testis to provide treatment and for a negative test is not to providetreatment. Consider a lab test that costs $10 where Sensitivity=85% andSpecificity=90%. Now referring to 500, the Expected Value of theTest=Cheapest Decision for each TestResult×Probability=($63.45×0.1375)+($20.38×0.8625)=$26.31. However, theExpected Value of not utilizing a Test=$35.95.

Referring now to FIG. 32B, a representative table for test utilizationin decision making analysis according to factoring in probabilityassessment as applied to a loss matrix (510), is shown. Progression oflogic is shown flowing in 510. In the example of the second test (510),given the probabilities shown, the cheaper decision for a positive testis still to provide treatment and for a negative test is still not toprovide treatment. Note the fact that the optimal choice according tothe two example loss matrices did not change even though the sensitivityand specificity for the first test (500) and the second test (510) aredrastically different. However, the sensitivity and specificity of atest still provides the basis for the optimal choice. Consider a labtest that costs $30 where Sensitivity=100% and Specificity=100%. Nowreferring to 510, the Expected Value ofTest=(0.05×$80)+(0.95×$35)=$37.25. The test shown, second test can beconsidered a perfect test based on the high sensitivity and specificity.Therefore the expected value of the second test is $37.25, of the firsttest is $26.31, and of no test utilization is $35.95. Further, considerthe concept of a cost free Perfect Test where the expected value of suchas test is $7.25. Using logic, a physician would likely turn $7.25 intoan Opportunity Loss, where no test utilization would have an expectedvalue of $28.70 (this represents the most one should pay for a perfecttest as it was an Opportunity Loss).

Referring now to FIG. 32C, a representative table for test utilizationin decision making analysis according to factoring in probabilityassessment as applied to a loss matrix (520), is shown. Progression oflogic is shown flowing in 520. In the example of the third test (520),given the probabilities shown, the cheaper decision for a positive testis to not provide treatment and for a negative test is also not toprovide treatment. Note in comparison the first test 500 and second test510 (refer to FIGS. 32A and 32B) the optimal choice did change as thesensitivity and specificity for the test shown (520) is different. Eventhough the sensitivity and specificity of a test still provides thebasis for the optimal choice, cost combined with total populationprobabilities remains considerations. Now consider a third test with aCost=$1, Sensitivity=60%, and Specificity=60%. Now referring to 510, theExpected Value of this third test=(0.41×$51.18)+(0.59×$26.42)=$36.57. Incomparison, the expected value of the second test was $37.25, the firsttest was $26.31, and no test utilization was $35.95. Remembering thatvalue is the same as cost, the overall optimal choice now becomes thefirst test 500 because no test is free of cost.

As an example, the detailed methods for Stage 4: Automated Analyzer (Afull spectrum of formulas to compute the statistics and resultsdesired), are as follows:

Stage 4 involves a full spectrum of formulas to automatically calculatedesired statistics and results (as an example, these formulas can bewritten as functions and stored in excel to be later applied tospreadsheets in an automated fashion as each spreadsheet in is createdfrom Stage 3: NLP engine). Work originally performed on various filesdated Dec. 11, 2005 to Mar. 27, 2006. The formulas can be calculatedwithout a unit of time as a denominator to provide an overall ratewithout reference to time. A given calculation can refer to the totaltabulated within a database irrespective of time. As an assumption, 0.1%is standard for inadequate for a variety of reasons, usually a technicallimitation. (As an example, improper timing of contrast administrationduring imaging protocol scan acquisition). With reference to AutomatedAnalyzer Follow-Up, in instances where patients who did not have a thescan in question, a surrogate maker can be searched for including butnot limited to imaging, operation/procedure/surgical note, clinical noteof death, etc.

Array Formulas as applied within the Automated Analyzer—Array formulasto perform this automation were created. Positivity rate and number ofnegative scans were automatically calculated. As an example 400 (referto FIG. 27), the following formula was used to auto-calculate thepositivity rate for Positive Acute Aortic Disorders (POSAAD).

1. =ROUND(SUM(LEN(AO7:AO481)-LEN(SUBSTITUTE(AO7:AO481,“POSAAD”,“”,1)))/LEN(“POSAAD”)/474,3)

As an example 410 (refer to FIG. 27), the following formula was used toauto-calculate the number of negative scans (NEG).

2. =SUM(LEN(AP7:AP481)- LEN(SUBSTITUTE(AP7:AP481,“NEG”,“”)))/LEN(“NEG”)

Modification and improvement to Array Formulas—Improved automatedcalculation run where total positive acute aortic disorders (POSAAD) was37.8%. The POSAAD decoder was checked for accuracy by comparing thedecoded results against a verified and validated reference standard, andsubsequently found to be highly accurate.

Further improvement of NLP engine and the Automated analyzer—Analyzingdecoded radiology language into a percentage using a single-cellone-dimensional vertical array formula. On Oct. 27, 2006, found thatAortic dissection protocol MDCT use increased each subsequent year. Theacute aortic disorder positive scan rate in 2004 was 37.8%

Sample size determination for reference standard statisticalsignificance—Formula to required sample size or number of report toachieve necessary error bound B and a 95% confidence interval, to verifynumber of examinations needed within the reference standard forstatistical significance. Given a defined positivity rate of 18% fromthe previously published 2002/2003 database (whereby reference to thefollowing is now incorporated, Radiology. 2006 March; 238(3):841-52.Epub 2006 February 1.). Further, hereby reference to InformationTechnology, Learning, and Performance Journal. 2001, 19(1) 43-50 is nowincorporated for mathematical details:

1. B = error bound = 1/sq (N) 2. if N = 400, then B = 5% with 95% CI V =variance = 18% as above N = sample size needed B = Error bound 3. N =4V/B pwr2 = 4(.18)/.05 power (2) = 388 sample size for 95% CI or B = 5%N = 400 gives B = 5% or CI = 95% 4. B = error bound = 1/squareroot (N)if N = 400, then B = 5% with 95% CI

Automated Analyzer—imaging protocol verification (post imaging protocolA examination)—The NLP engine can be applied to a database tofind/replace any mention of acceptable terms defining the desiredimaging protocol A. The NLP engine can then be applied an additionaltime to a copy of the database to find/replace mention ofinadequate/incomplete imaging protocol A examinations (as an example,the aortic dissection protocol CT requires first a non-contrast scan,and then a contrast scan). The resulting databases from steps “a” and“b” can compared, combined, then be parsed/coded into (A) reports fromthe desired imaging protocol A that were adequately completed and (B)reports from other undesirable imaging protocols orinadequate/incomplete imaging protocol A examinations. The reports fromany other undesirable imaging protocols or inadequate/incomplete imagingprotocol A examinations can either be deleted from the database orignored from all subsequent analysis. As an additional measure ofquality control, the use of a variety of phrase “find” iterations todefine imaging protocol A can be employed if inadequate application ofStage 1 methods were utilized. As examples, aortic dissection protocolMDCT and AAA protocol MDCT. Note that this fact alone can be exploitedin instances where a pre-existing keyword searchable database does notexist and cannot be used to search a clinical database. The genericpower of Stages 2 through 4 can override the deficit of Stage 1.

Automated Analyzer—Appropriateness Gauge (pre or post imaging protocol Aexamination)—This capability allows automated indication (ChiefComplaint (CC), history, clinical history, history of present illness)analysis for requested/written imaging protocols. Note that many ofthese methods as described can also be applied to an Automated AnalyzerFollow-Up, as described below. The NLP engine can be applied to adatabase containing imaging reports and clinical notes to extractacceptable indications PRIOR to (in a real-time manner) performing animaging protocol. In a similar fashion, the decoding engine can beapplied to a database containing imaging reports and clinical notes toextract acceptable indications AFTER (in a retrospective manner)performing an imaging protocol. Acceptable indications for an imagingprotocol can mirror standard criteria as published in the literature.The results for the decoded imaging protocol indication can then besubjected to successive steps (as mentioned previously, Stage 3, Step11-d) to find the phrase in question and replace the word, rather thancell, in an iterative fashion. The resulting cell (containing themulti-step decoded report) may have “several phrases” corresponding to“replaced phrases.” The summation of all of these “replaced phrases”within the single cell can be split in a binary fashion (as mentionedpreviously, Stage 3, Step 11-d) providing a decimal “grade” of howlikely the indication is based on how many times “the find phrase” ismentioned throughout the clinical record (emergency medicine note,clinic notes, consultant notes, admission notes, etc.) immediatelypreceding the requested imaging protocol in chronological time. Thisallows the percentage grading of each acceptable indication (by ACRappropriateness criteria, as an example) for the requested imagingprotocol to be analyzed in an automated manner either before, or after,the requested imaging protocol is completed. The analyzed results can becombined and displayed as an overall “appropriateness gauge” for theimaging protocol in question to any individual (requesting physician,referring physician, radiology technician, radiologist, etc.) to serveas a quick check if the imaging protocol is meets standardappropriateness criteria. As an example, some ordering physicians do notorder the appropriate radiologic examination based on the indicationprovided. As an example method, the terms “CT head” for an indication ofeither Cerebrovascular Accident (CVA) or mass can be searched and theterms “MR head” for an indication of either CVA or mass can be searched.As an example method, all emergency patients with clinical symptoms andsigns suggesting appendicitis during 2008 and 2009 can be searched asapplied to the EMR. As an example method, cross-reference MRexaminations performed with the indication provided by the orderingphysician; A subset of MR examinations performed, such as MagneticResonance Cholangiopancreatography (MRCP), can be compared to theindication provided. As an example method, ER orders for radiologicexaminations include well defined indications, ER orders for admissionand discharge also include well defined diagnoses, sometimes theindications for radiology examination and final diagnoses for admissionand discharge are discrepant therefore the indication within theradiology report can be compared to the ER admission and dischargediagnoses. As examples of actionable results obtained from the methodsdescribed, (A) The resulting data can help provide feedback to referringphysician who routinely order inappropriate examinations based on theindication. (B) Overall improvement in patient care will be supported byensuring only appropriate examinations are performed. (C) The resultsare also mandatory for the formulas in the automated positivity gaugecategory and automated validation imaging protocol A category tofunction appropriately and accurately. (D) The resulting data willdemonstrate how frequent the indication for a radiologic examination andthe admission or discharge diagnoses are discrepant and appropriateaction can be taken to ensure the ER does not order radiologicexaminations when they are not indicated.

Automated Analyzer—imaging protocol gauges—The capability is based onthe fact that imaging protocols are designed to accurately diagnose oneparticular disease or spectrum of diseases (as an example, aorticdissection protocol CT) unless the imaging protocol design isgeneralized to focus on speed, efficiency, and screening for a widespectrum of unrelated diseases (As an example, non-contrast protocol CTof the abdomen and pelvis in emergency cases with little to no precedingclinical workup). Positive scan rates (positivity rates) can begenerated for all imaging protocols, provided the design intent isunderstood. To simplify creation of positivity rates, all imagingprotocols can be categorized as positive or negative for an abnormalfinding, this allows for extraction of serendipitous identified positivefindings (those findings that are either not the focus of the protocolor non-acute in timing of disease). In addition, the concept ofstability is used in radiology when an imaging finding is similar incharacterization when compared to any prior imaging. This can besearched for to further sub-categorize positive imaging findings. As anexample, aortic dissection protocol CT is designed to diagnose acuteaortic diseases (AAD) including aortic dissection, intramural hematoma,penetrating aortic ulcer, aortic aneurysms, and aortic rupture. All ofthe diseases may be identified in any combination and at any time pointon the natural disease course. The natural disease course is usuallycategorized as acute, sub-acute, or chronic. Further, serendipitouslyidentified diseases such as Renal Cell Carcinoma (RCC) may be diagnosed,but the protocol designed is not optimized to properly and completelydiagnose this disease. Regardless, the finding and partial diagnosis ofRCC made possible by using the aortic dissection protocol CT can allowthe overall imaging examination to be categorized as a positive scan,however positive for a chronic finding and/or an alternatefinding/diagnosis. This alternate diagnosis may or may not actuallyrepresent the source/etiology of the patient's Chief Complaint orcorrespond to the referring physician's original indication for orderingthe imaging protocol. The radiology literature suggests that thesealternative findings be categorized as alternate diagnoses and aspositive scans, but clearly labeled as positive scans for alternativediagnoses, not positive for disease A when the imaging protocol B is notdesigned to properly and completely diagnose that given disease A (as anexample, imaging protocol B is designed to diagnose disease B, notdisease A).

Automated Analyzer—imaging protocol performance rate—This capability isbased on calculating the imaging protocol accuracy as compared to areference standard (as an example, aortic dissection protocol CTcompared to surgical reports and/or pathology reports).

Automated Analyzer—imaging protocol performance gauge—This capability isbased on calculating the imaging protocol accuracy as compared to areference standard (for gauge normalization, hereby normalizationapplies to all subsequent reference to gauges).

Automated Analyzer—requested/written imaging protocol positivitygauge—This capability is based on calculating the requested/writtenimaging protocol positivity rate as compared to a reference standard.

Automated Analyzer—imaging protocol positivity gauge—This capability isbased on calculating the imaging protocol positivity rate as compared toa reference standard. This gauge may only be relevant for advancedimaging protocol such as MDCT and magnetic resonance imaging (MRI).

Automated Analyzer—imaging protocol overall positivity gauge—Thiscapability is based on calculating the imaging protocol overallpositivity rate as compared to a reference standard.

Example formulas/functions include:

-   -   1. Actual quantity of requested/written imaging protocol        A=(quantity of requested/written imaging protocol A)−(accepted        standard % for inadequate imaging protocol A×quantity of        requested/written imaging protocol A)−(quantity of        requested/written imaging protocol A that did not have imaging        protocol A performed)    -   2. Actual quantity of requested/written imaging        protocol=quantity of requested/written imaging protocol that was        actually scanned or completed    -   3. Actual quantity of requested/written imaging protocol        A/quantity of requested/written imaging protocol A=Performance        rate for requested/written protocol A

Performance rate for requested/written protocol A also referred to as“imaging protocol performance rate” or “imaging protocol performancegauge.”

-   -   4. Number of positive scans from imaging protocol A/Actual        quantity of requested/written imaging protocol A=Positive scan        rate for imaging protocol A

Positive scan rate for imaging protocol A referred to as positivity ratefor imaging protocol A, “imaging protocol positivity rate,” or “imagingprotocol positivity gauge.”

-   -   5. Positive scan rate for imaging protocol A+Positive        alternative diagnosis rate for imaging protocol A=Overall        positive scan rate for imaging protocol A=“imaging protocol        overall positivity gauge”    -   6. Number of positive scans from imaging protocol A/quantity of        requested/written imaging protocol A=Positive scan rate for        requested/written imaging protocol A

Positive scan rate for requested/written imaging protocol A referred toas positivity rate for requested/written imaging protocol A, or“requested imaging protocol positivity rate,” or “requested/writtenimaging protocol positivity gauge.”

The result “positivity rate for requested/written protocol A” can belimited or expanded in scope:

-   -   7. If limited to a referring physician: positivity rate for        requested/written protocol A=positivity rate for        requested/written protocol A for that particular        referrer=Referrer positivity rate    -   8. Referrer positivity rate=Referrer positivity gauge for        imaging protocol A

As examples, the utilization (positivity rate) of imaging protocols byordering/referring physician. As an example method, appendicitisprotocol CT can be searched for and the positivity rate identified. Asexample results, the values obtained can be limited to radiologist X,department X, hospital X, referring physician, etc. As an examplemethod, lower extremity protocol CT (in the setting of trauma) istrending towards over-utilization as a result of the ease of ordering CTto rule-out a highly morbid condition after negative radiographs,indeterminate radiographs, or no prior imaging. As example results, thevalues obtained can be limited to radiologist X, department X, hospitalX, referring physician, etc.

Automated Analyzer—referral tracking—This capability is based onreferrer imaging protocol A scan volume. As conceptual examples,radiology administration and marketing managers do not want to wait anentire month or quarter to identify decreases in referrals ofhigh-dollar modalities such as MRI and CT. As a conceptual example,departmental chairmen want to be able to identify physicians whoover-utilize or underutilize imaging protocols in their diagnosticevaluations. As an example method, search, restructure/parse, decode,and analyze (Stages 1-4) to identify all referring clinicians by volume,date and modality. As examples of a method and the result, in QualityImprovement (QI) and/or Quality Assurance (QA) for referring physicianson imaging protocol use Stages 1-4 and identify referring physicians whoorder unnecessary imaging protocols for simple headache as identified inthe EMR. As examples of a method and the result, Stages 1-4 can be usedto identify referring physicians who order pulmonary embolism protocolCT without prior documentation of moderate or high pre-test probabilityof disease. As examples of a method and the result, Stages 1-4 can beused to identify avoidance of routine pre-operative chest radiographs inambulatory patients with an unremarkable history and physicalexamination. As examples of a method and the result, Stages 1-4 can beused to identify and encourage avoidance of appendicitis protocol CT inpatients less than 18 years old without initial consideration ofultrasound examination. As examples of a method and the result, Stages1-4 can be used to identify and encourage avoidance of follow-up imagingprotocol examination of uncomplicated clinically incidental adnexalcysts.

Automated Analyzer—Referrer scan volume for imaging protocol A—Thiscapability is based on referrer scan volume for imaging protocol A, theresult contains important trend information. The scan volume is asurrogate marker for the combined effects of the personal threshold forrequesting imaging protocol A, the clinical severity, and the trueincidence of disease in the given population.

-   -   9. Referrer scan volume=“threshold for request/written imaging        protocol A”+clinical severity+true incidence of suspected        disease    -   10. If limited to a radiology department: positivity rate for        requested/written protocol A=positivity rate for        requested/written protocol A for that particular radiology        department=radiology department positivity rate    -   11. Radiology department positivity rate=radiology department        positivity gauge for imaging protocol A    -   12. Imaging protocol A scan volume trends over a given period of        time within the same population (department, as an example)        demonstrate either referrer threshold changes, clinical severity        changes (a random change), or true changes in incidence of        disease within a population.    -   13. If limited to a region or city: positivity rate for        requested/written protocol A=positivity rate for        requested/written protocol A for that particular region or        city=Regional or city positivity rate    -   14. Regional or city positivity rate=regional or city positivity        rate for imaging protocol A        When “A” is defined as a given disease entity (as an example,        aortic dissection) then the following can be applied: If this        imaging protocol A has had proper validation study performed,        then the regional or city positivity rate can serve as a        surrogate marker for the incidence of disease A in that        particular region or city. In this context, incidence is defined        as the number of new instances of disease A per unit time in        that particular region. If this imaging protocol has had proper        validation study performed, then the Regional or city positivity        rate can serve as a surrogate marker for the prevalence of        disease A in that particular region or city. Whereby prevalence        is defined as the total number of disease cases A in that        particular region. If the positivity rate for requested/written        protocol A from hospital X is compared to the positivity rate        for requested/written protocol A from hospital Y, the difference        can be calculated:    -   15. (positivity rate for requested/written protocol A from        hospital X)−(positivity rate for requested/written protocol A        from hospital Y)=positivity rate difference        The closer the positivity rate difference is to zero, the more        equivalent the utilization for imaging protocol A are for        hospital X and hospital Y. Whereby the positivity rate for        requested/written protocol A from hospital X is referred to as        the “imaging protocol A utilization rate from hospital X.” The        positivity rate for requested/written protocol A from hospital Y        is referred to as the “imaging protocol A utilization rate from        hospital Y.” The difference is referred to as the “imaging        protocol A utilization rate difference between hospital X and        hospital Y.” As an example, the difference in utilization rate        between hospitals can serve to demonstrate where imaging        protocol is over-utilized, under-utilized, or appropriately        utilized when three or more hospitals are compared.    -   16. (Total quantity of requested/written imaging protocol A from        hospital X)−(Total quantity of requested/written imaging        protocol A from hospital Y)=utilization difference of imaging        protocol A between hospital X and hospital Y        The result “positivity rate for imaging protocol A” can be        limited or expanded in scope:    -   17. If limited to radiologist Z: positivity rate for imaging        protocol A=positivity rate for imaging protocol A for        radiologist Z=Total positivity rate for radiologist Z for        imaging protocol A    -   18. If limited to radiologist D: positivity rate for imaging        protocol A=positivity rate for imaging protocol A for        radiologist D=Total positivity rate for radiologist D for        imaging protocol A        Now we can compare either the total positive scans (when the        actual quantity of requested/written imaging protocol A read is        not given) and/or the total positivity rate (when the actual        quantity of requested/written imaging protocol A read for        radiologist D and radiologist Z is given) for radiologist D and        radiologist Z for imaging protocol A.    -   19. (Total positive scans for radiologist Z for imaging protocol        A)−(Total positive scans for radiologist D for imaging protocol        A)=Total positive scan difference between radiologist Z and        radiologist D for imaging protocol A        Now we can compare the total positivity rate for radiologist D        and radiologist Z for imaging protocol A.    -   20. (Total positivity rate for radiologist Z for imaging        protocol A)−(Total positivity rate for radiologist D for imaging        protocol A)=Total positivity rate difference between radiologist        Z and radiologist D for imaging protocol A        The concept can be expanded to create a reading radiologist        Receiver Operating Characteristic (ROC) curve for QI purposes        when the positivity rate and negativity rate are plotted.    -   21. Imaging protocol positivity rate for department (per year)        compared to radiologist ROC    -   22. Imaging protocol positivity rate for department (per year)        compared to ER physician imaging protocol utilization

Automated Analyzer—process for validation of each imaging protocol—Thiscapability is based on a method to address problems with definingsensitivity, specificity, positive predictive value, negative predictivevalue, and accuracy for each imaging protocol, in an efficient manner.When Stages 1-4 are applied to given imaging protocol with comparison toa more accepted reference standard such as findings within a surgical orpathology report, the validation can be performed.

Automated Analyzer—problems with defining “requested/written imagingprotocol A”=suspected cases (patients) disease A. This capability isbased on the following assumption that some referring physicians favorspeed for patient evaluation at the expense of appropriate imaging. Theresult is that some requested/written imaging protocol A has noclinically appropriate indication to undergo imaging protocol A. So eventhough inadequate imaging protocol A+performed imaging protocolA=requested/written imaging protocol A, this is not the total cases ofsuspected disease A. Even though using logic the aforementioned holdstrue mathematically, but human error does occur and therequested/written imaging protocol is not appropriate for the suspecteddisease. This error effects the Automated Analyzer validation of imagingprotocol A. Specifically, the error alters positivity rate or positivescans for imaging protocol A. To adjust for the concept of“inappropriate indication”, the adjusted formula is:

-   -   23. inadequate imaging protocol A+performed imaging protocol        A=requested/written imaging protocol A    -   24. (reasonable CC/indication)/(requested/written imaging        protocol A)=appropriate requested/written imaging protocol A

Now more accurate calculations for validation imaging protocol A can beperformed. Now calculate automated positivity rate or positive scansimaging protocol A with 2×2 contingency statistics and accuracy comparedto reference standards (any follow-up imaging, surgical notes,pathological notes, clinical notes, etc.) When a given imaging protocolA is not considered to be the diagnostic reference standard for diseaseA, an automated validation analysis of the imaging protocol can beperformed by comparing the entire database (hospital, department, clinic. . . etc.) of imaging protocol A to the EMR. By working backwards fromthe specific imaging protocol A examination identified in the originalanalysis to create the total positivity rate, the same Medical RecordNumber (MRN), or patient name, can be used to identify the correspondingrecord in the EMR. Once this correlation is completed, an analysis ofthe entire clinical record for that MRN/patient/case can be decoded andanalyzed for disease A by applying the NLP decoding engine to the EMR.Specific areas that would be analyzed include, but are not limited to,all follow-up/subsequent imaging, surgical notes, pathology notes,clinical notes, discharge summaries, discharge diagnosis, dischargeInternational Classification of Diseases (ICD) codes, etc. This analysisallows automatic creation of a 2×2 contingency table and calculation ofthe overall accuracy for a diagnosis for a given imaging protocol Aafter all corresponding MRN/patient/cases are analyzed. The resulting2×2 contingency table defines the number of true positives, falsepositives, false negatives, and true negatives for that particularimaging protocol when assessing for disease A. The result is anautomated process for validation of each imaging protocol.

The aforementioned concept of error introduced by inappropriaterequested/written imaging protocol A can also be applied to the conceptof error introduced by inadequate imaging protocol examinations as well(as an example, when a non-contrast examination is performed when acontrast examination is appropriate). If the same concept to automatethe validation statistics for an imaging protocol is limited in scopeand only applied to particular radiologist, a 2×2 contingency table, andoverall accuracy for a diagnosis, for that radiologist can be created.Specifically, when limited to radiologist Z for imaging protocol A, thenumber of true positives, false positives, false negatives, truenegatives, and overall accuracy can be defined. Automation of above thedescribed process for a summative ROC curve and/or overall accuracyanalysis for diagnosis A for radiologist Z can then be applied. When thesame concept is applied to every imaging protocol radiologist Z reads, aseries of data points corresponding to sensitivity (true positivityrate) and (1—specificity, or false positivity rate) can be created andplotted where sensitivity corresponds to the Y axis and (1—specificity)corresponds to the X axis. In a similar fashion, Positive PredictiveValue (PPV) and Negative Predictive Value (NPV) can be calculated. Theresult is automated and summative ROC curve, and overall accuracy fordiagnosis A, for radiologist Z.

-   -   25. Where PPV=true positive/(true positive+false positive)    -   26. NPV=true negative/(true negative+false negative)        When same concept is expanded in scope to department Z for        imaging protocol A, the number of true positives, false        positives, false negatives, true negatives, and overall accuracy        can be defined for department Z. When the same concept is        limited in scope and applied separately to each radiologist for        a given department (as an example) for given imaging protocol A,        a series of data points corresponding to sensitivity (true        positivity rate) and (1—specificity, or false positivity rate)        for each radiologist for imaging protocol A can be created and        plotted where sensitivity corresponds to the Y axis and        (1—specificity) corresponds to the X axis. In a similar fashion,        Positive Predictive Value (PPV) and Negative Predictive Value        (NPV) can be calculated (as shown above). The result is        automated and summative ROC curve for imaging protocol A for the        entire department. In other words, a location specific        sensitivity and specificity for a given imaging protocol can be        created and automated, thus allowing referring physicians to see        real time what the true value of an imaging protocol is prior to        ordering/requesting the imaging protocol for a patient. When        this information is provided and taken into consideration with        the pre-test probability of disease A (or pre-test odds) for        given patient/case, most of the biostatistics in medicine become        far more usable and can realistically guide further guide        medical decision making in real-time. Further, a 2×2 contingency        table, and overall accuracy can be defined for department Z. The        primary limitation of the above methods of creating an imaging        protocol positivity gauge is that projection of the resulting        data to other populations may be confounded by a different        incidence rate for a given disease.

Automated Analyzer—Automated follow-up analysis—This capability is basedon the fact that the result “positive scans from imaging protocol A” canbe categorized according to each positive scan (MRN/patient/case). Eachpatient (MRN/patient/case) can then have their EMR searched for anycorresponding follow-up (including imaging protocol A, any other imagingprotocol, clinical notes, emergency medicine notes, surgical orprocedural notes, pathology notes, etc.) data mined (searched) for thesame (or similar) diagnosis using the NLP decoding engine. Thosepatients (MRN/patient/case) without follow-up of any kind can beidentified (especially when diagnosis A is considered life-threateningor potentially life-threatening at a more advanced stage). Onceidentified, these patients can be referred back to the originalcorresponding referrer, a new more appropriate physician, insuranceprovider, or even a family member for follow-up clinical or surgicalcare as needed. As examples, diagnoses with potential need for furtherfollow-up diagnostic imaging or interventional radiology procedures:

-   -   27. Osteoporosis screening such as Dual-Energy X-ray        Absorptiometry (DEXA) scanning in the elderly    -   28. Inferior Vena Cava (IVC) filter placement follow-up (cases        for that lack needed placement or follow-up for potential        removal    -   29. Abdominal aortic aneurysm or other acute aortic disorder        without follow-up diagnostic imaging or vascular repair    -   30. Following up imaging on pulmonary nodules of appropriate        size or imaging characteristics based on standard criteria    -   31. Follow-up on mammographic imaging (MAM)    -   32. Diagnoses identified from a search of the entire EMR without        the following recommended neurosurgical/orthopedic procedural        care (as examples, discectomies, laminectomies, vertebral        corpectomies, and insertion/repair of neurostimulators)

Patients diagnosed as obese, or as having elevated an Body Mass Index(BMI), or elevated body weight, without a history of the followingpossible procedures can be identified by searching the entire EMR andreferred back to the Primary Care Physician on record or otherappropriate caregiver to potentially have the following proceduresperformed (if necessary after appropriate consultation). As examples,hernias (epigastric/incisional/inguinal), laparoscopiccholecystectomies, and laparoscopies (gastric banding).

Patients diagnosed with high grade orthopedic disease (such as highgrade Anterior Cruciate Ligament (ACL) injury, as an example) byclinical or imaging examinations after searching the EMR with the NLPdecoding engine who do not have clinical, surgical/procedural, orimaging follow-up after a given period of time can be identified andreferred back to the original referring physician, primary care giver,or other appropriate care giver. As examples, femorotibial jointpathology, glenohumeral joint pathology, and bone tumors.

Patients identified from a search of the entire EMR/RIS/PACS with knownclinical or radiologically diagnosed disease severity warranting imagingor procedural follow-up care. As examples, diagnoses with potential needfor further follow-up diagnostic imaging or interventional radiologyprocedures. As an example, an automated virtual safety net can becreated using Stages 1-4 to identify cases for QA Coordinator/ClericalSupport/Referring Clinician where both immediate and 12 month follow upis warranted. As an example, using Stages 1-4 a virtual referralanalyzer can be created for patients with risk factors for a highmorbidity or mortality disease can be identified by a keyword search ofthe EMR/RIS/PACS and follow-up can be provided by alerting the PrimaryCare Physician (PCP) of recommended diagnostic or interventional care.In addition, recent and emerging recommendations can be added andincluded. As a detailed example, patients with known risk factors forAlzheimer's disease (advanced age, family history, etc.) can beidentified and new recommendations for Positron Emission Tomography(PET) screening can sent to the PCP

-   -   33. After identifying Imaging protocol A positive cases (as        detailed above)→look for positive cases without any form of        follow-up        As an example, using Stages 1-4 the term carotid ultrasound and        percentage (grade) stenosis can be identified. As an example,        diagnoses identified from a search of the entire EMR without the        following recommended neurosurgical/orthopedic procedural care.        As an example, diagnoses of risk factors such as smoking in        males over the age of 65 (65-75) without AAA screening in the        HIS/EMR can be flagged and referred back to their PCPs for need        of appropriate diagnostic imaging for disease        prevention/screening. Patients diagnosed as obese without a        history of weight reducing procedures can be identified by        searching the entire EMR and referred back to the PCP to have        the procedures performed. Patients diagnosed with high grade        orthopedic disease after searching the EMR with the NLP decoding        engine who do not have any follow-up after a given period of        time can be identified and referred back to the original        referring physician.        As an example, using Stages 1-4 to identify follow-up imaging in        EMR/PACS/RIS for a given patient, the results can be compared to        the prior imaging report for agreement/concordance. This concept        does not require the same exact prior imaging protocol as the        initial imaging protocol may be designed for screening only.        Further, the same exact imaging protocol may not always be        performed by the same department across hospitals. As an        example, cardiac imaging may not be performed in radiology,        sometimes Transthoracic Echocardiogram (TTE) or Transesophageal        Echocardiogram (TEE) is done by cardiology in a separate        database and fed via HL7 into the EMR.

Automated Analyzer—prevention analyzer—This capability is based onreferring to known well established prevention imaging recommendationsas a knowledge base. As a detailed example, using Stage 1-4 to identifymen with the following parameters: a given age, and known smoker,coupled with this a search within the radiology PACS/RIS for lack ofneeded imaging. The results can be the basis for referrals to radiologyfor AAA ultrasound imaging (US) (or a DEXA scan for women, MAM, etc.)The results serve as both Quality Improvement and increasedreimbursement/incentives for diagnostic imaging departments orhospitals.

Automated Analyzer—Automated disease progression tracker—This capabilityis based on disease diagnosed in radiology reports in a patient-centricmanner, (as an example, comparing prior radiology report to a morerecent radiology report). As an example, cross-reference bone scanresults with Prostate-Specific Antigen (PSA) levels. First incorporate aseparate patient laboratory database containing PSA levels, where theterm PSA can be searched, then the term bone scan be searched inPACS/RIS. The results can be cross-referenced to compare bone scanresults with PSA levels thereby improving radiology report qualityovertime through QA measures.

Automated Analyzer—Automated ionizing imaging protocol repetitiontracker. This capability is based on redundant and/or unnecessaryradiology procedures ordered, leading to overexposure to the patient andpotentially uncompensated costs. An appropriate need for follow-upwarrants escalation of communication efforts to guarantee the patientreceives care. As an example method, using Stages 1-4 to identify allreports with “Recommend Follow-up” in the Impression section, theresults can be quickly scanned for appropriateness of findings. Thoseradiologists or referring clinicians with undesirable trends can beidentified and coached for improvement. Further, patient follow-up canalso be incorporated at this point. As an example, using Stages 1-4identification of “standards of follow-up” can be defined for radiologicexaminations for a given diagnosis. As an example, patients with knownnephrolithiasis can be analyzed to determine the average number offollow-up examinations over a 3 month period. As an example method,using Stages 1-4 to identity the term nephrolithiasis and the resultingexaminations over a 3 month time frame can be further analyzed based onhow many follow up examinations were performed and the results can thencompared. The results will demonstrate the range in the number offollow-up radiologic examinations and help define the average. Aguideline to help future follow-up examinations can be implemented.

Automated Analyzer—unnecessary follow-up—This capability is based on thefact that patients identified from a search of the entire EMR/RIS/PACSwith an immediate history of interventional surgical procedure and withnegative clinical or imaging protocol follow-up may not need furtherimaging or clinical follow-up. As an example, using Stages 1-4 toidentify post percutaneous Transthoracic Needle Biopsy (TNB) patientswho do not reveal a pneumothorax immediately post procedure and theresults can be analyzed for trends for procedures where the currentstandard for imaging protocol follow-up may be unnecessary. As anexample result, if all of the above TNB cases have negative long-termimaging follow-up with or without short-term nurse monitoring, then bothshort-term imaging protocol and long-term imaging follow-up may beconsidered unnecessary.

Automated Analyzer—Automated radiation dose tracker—This capability isbased on tracking radiation dose levels over time or tracking radiationdose levels per examination will improve patient safety as this data isnot currently monitored routinely, however is extremely important givenrecent high profile hospital failure and lawsuits nationally.

-   -   34. Automated imaging radiation dose tracker per patient (for        accumulated dose)

Automated Analyzer—Radiation trends—This capability is based onaccumulated radiation dose to ensure safe radiation dosage levels bytracking patients who have had multiple scans performed over the courseof three months. A determination if all the scans were necessary is madeand a determination if CT is over-utilized in the emergency room/setting(ER) is made. As examples, the following imaging protocols can beassessed: Renal Stone protocol CT, Pulmonary Embolism protocol CT, andCVA protocol CT. As an example method, using Stages 1-4 to identify thefollowing parameters over a three month period, patients where the aboveimaging protocol were used, referring physician, and diseaseprogression. The following function/formula is then applied:

-   -   35. Accumulative radiation dose/patient/time=Ensuring safe        radiation dosage levels by tracking patients who have had        multiple scans performed over the course of given time period    -   36. Radiation exposure per examination compared to expected        levels        As an example, the resulting data may show that some spikes in        imaging are necessary for tracking disease progression in the        ER, but other spikes are caused by different physicians ordering        similar studies. Appropriate administrative action can then be        taken based on actual “in house” hospital data, not outside        published data.        As an example, radiation exposure or examination QI/QA where the        correct amount of radiation exposure is important. In order for        a chest radiograph to be diagnostic, a certain amount of        radiation is needed. Too little yields a non-diagnostic exam.        Too much yields a non-diagnostic exam and unnecessary patient        exposure. As an example method, using Stages 1-4 to identify the        radiation dose for each examination in the DICOM, over a given        time period, each examination can be categorized as utilizing        too little, appropriate, or too much radiation. As an example of        the results, Quality Assurance for appropriate patient exposure        during chest radiographs can be analyzed. Further, appropriate        action can be taken to improve or maintain chest radiograph        Quality Assurance.    -   37. Automated imaging radiation dose for desired range

As an example, using Stages 1-4 to identify fluoroscopic examinations(FL) that result in highest patient radiation exposure over a given timeperiod both the final fluoroscopic time and the final patient radiationdose in units of dose area product (yGy*m2) can be calculated. As anexample of the results, the examinations with both the highestfluoroscopic time and radiation dose can be presented to the appropriatepersonnel to improve awareness regarding high risk procedures thathistorically result in higher fluoroscopy times and radiation doses tohelp reverse radiation trends.

Automated Analyzer—imaging efficiency measures (primarilyoutpatient)—This capability is based on imaging efficiency as comparedto known standards. As an example, mammography follow-up rates have adefined appropriated level. As an example method, using Stages 1-4 toidentify patients with a Diagnostic Mammography or Ultrasound of theBreast study following a Screening Mammography Study, the resultingpercentage can be calculated. Using these results, a readingradiologist's inability to adequately determine when additional imagingis necessary can be determined.

-   -   38. Mammography Follow-Up Rate=Patients with a Diagnostic        Mammography or Ultrasound of the Breast Study following a        Screening Mammography (given time period)/Patients with a        Screening Mammography study        As an example, using Stages 1-4 to identify contrast use during        Abdomen CT the resulting percentage of contrast to non-contrast        examinations can be calculated. As an example result, a higher        value indicates a high use of contrast examinations and raises        questions of inefficient ordering of imaging protocols. Note        this concept only applies to screening protocol examinations.    -   39. Percentage of contrast enhanced Abdomen CT=The number of        Abdomen CT studies with contrast (or combined with and without        contrast)/The number of Abdomen CT studies performed (with        contrast, without contrast, and both combined)        As an example, using Stages 1-4 to identify contrast use during        Chest CT the resulting percentage of contrast to non-contrast        examinations can be calculated. As an example result, a higher        value indicates a high use of contrast examinations and raises        questions of inefficient ordering of imaging protocols. Note        this concept only applies to screening protocol examinations.    -   40. Percentage of contrast enhanced Chest CT=the number of        Thorax CT studies with contrast (or combined with and without        contrast)/the number of Thorax CT studies performed (with        contrast, without contrast, or both combined)

Automated Analyzer—Centers for Medicare & Medicaid Services (CMS)Quality Measures—This capability is based on meeting known reportingrequirements for reimbursement. As an example, using Stages 1-4 toidentify and analyze the following terms: (A) MRI and Low back pain, (B)CT with contrast and without contrast, (C) CT with contrast and withoutcontrast in patients over 60 years old, and (D) mammogram in patientsover 60 years old. These results can then be compared to any follow-upexaminations corresponding to the same patient over a given time framewhere the call-back rate can be calculated. As an example, using Stages1-4 the terms breast ultrasound and probably benign in the same reportcan be identified and the rate of breast ultrasounds with the impressionof probably benign can be calculated. As an example, using Stages 1-4,the terms CT and MRI head for an indication of either CVA or mass can beidentified and positivity rates calculated. As an example, using Stages1-4 the terms pneumothorax, lung biopsy, and thoracentesis can beidentified and positivity rates calculated. As an example, using Stages1-4 the terms contrast extravasation can be identified. As an example,using Stages 1-4 the term carotid ultrasound and % stenosis can besearched where positive cases can be categorized as to how the positivefindings are reported (which criteria are used).

Automated Analyzer—Automated imaging protocol costs—This capability isbased on the fact that a disease can diagnosed by multiple imagingprotocol and all costs can be rounded to the nearest dollar using theArithmetic Rounding algorithm. As reference for the following, theDisease to be diagnosed is “disease C” by either “imaging protocol A” or“imaging protocol B.” Using this concept, the followingformulas/functions can be applied:

-   -   41. Actual hospital cost per imaging protocol=actual variable        direct cost per unit+actual fixed direct cost per unit+actual        fixed indirect cost per unit    -   42. Actual hospital cost per imaging protocol×quantity of        cases/unit time=Actual hospital cost for imaging protocol to        evaluate suspected cases during a given unit time    -   43. Where quantity of cases=# of cases the imaging protocol was        requested by a referring physician, Unit time=calendar year (as        an example)    -   44. Actual hospital cost for imaging protocol A to evaluate        suspected cases during a given unit time−Actual hospital cost        for imaging protocol B to evaluate suspected cases during a        given unit time=difference in imaging protocol cost during a        given unit time    -   45. (Actual hospital cost per imaging protocol A)×(quantity of        cases/unit time)/positive scan rate=Actual hospital cost for        diagnosing disease C using imaging protocol A during a given        unit time    -   46. (Actual hospital cost per imaging protocol B)×(quantity of        cases/unit time)/positive scan rate=Actual hospital cost for        diagnosing disease C using imaging protocol B during a given        unit time    -   47. Actual hospital cost for diagnosing disease using imaging        protocol A during a given unit time)—(Actual hospital cost for        diagnosing disease using imaging protocol B during a given unit        time)=Cost difference of diagnosing disease using imaging        protocol A compared to imaging protocol B during a given unit        time=Imaging cost savings to diagnosis disease C

Automated Analyzer—Automated imaging protocol billing/coding—Thiscapability is based on the fact that billing errors include not billingfor all examination performed or billing for the wrong examinationresults in lost revenue within the department of radiology. As anexample method, using Stages 1-4 to identify the radiologic examinationas routinely reported within the “examination” or “study” header withinthe radiology report and the type of radiologic examination (imagingprotocol A, as an example) is also included within the examination fieldentered by the radiology technician available through the RIS/PACS. Thebilled examination is located in a separate field based on ICD coding.Then identify the examination performed and compare this to the type ofexamination actually billed for chest CTs during a given time period. Asan example method, using Stages 1-4 to identify the type of MRIexamination performed in included within the radiology report ascompared to the type of MRI examination billed in included with aseparate billing database. The results containing discrepant billing canbe presented. The comparison of examinations performed and billedexaminations will assure appropriate billing and avoid lost revenue.Further, measures can be implemented to improve MRI billing when theexact causes of incorrect billing are identified.

Automated Analyzer—decrease professional/general liability risk exposureand increase quality of medical care/Quality Improvement (QI)—Thiscapability is based on meeting known Quality Improvement and QualityAssurance initiatives (QI/QA). As an example, detailed case logs for allradiology residents are a national requirement. As an example method,using Stages 1-4 to identify all modalities by time frame, by body part,and by radiologist the results can automatically serve as a case log.This information can be placed into a spreadsheet and organized by levelof detail required.

Automated Analyzer—Centers for Medicare & Medicaid Services (CMS)Physician Quality Reporting Initiatives (PQRI)—This capability is basedon the fact that CMS Physician Quality Reporting Initiatives (PQRI)reporting is currently accomplished manually at most hospitals. As anexample method, using Stages 1-4 to identify the terms “Fluoroscopytime” can be searched during a given time period and the results can becategorized by examination type and then analyzed for each examinationcategory. As an example method, using Stages 1-4 to identify the terms“breast ultrasound” and “probably benign” can be searched during a giventime period and the rate of breast ultrasounds with the impression of“probably benign” can be calculated. When data from a reference billingdepartment database during the same time period is used as a comparisonstandard, the accuracy can be analyzed. As an example method, usingStages 1-4 to identify the terms MRI and Low back pain, the terms CTwith contrast and without contrast, the terms CT with contrast andwithout contrast in patients over 60 years old, and the terms mammogramin patients over 60 years old can be identified.

As an example method, using Stages 1-4 to identify Stroke and StrokeRehabilitation as applied to CT or Magnetic Resonance Imaging (MRI)Reports where the percentage of final reports for CT or MRI studies ofthe brain performed either: (A) In the hospital within 24 hours ofarrival or (B) In an outpatient imaging center to confirm initialdiagnosis of stroke, transient ischemic attack (TIA) or intracranialhemorrhage are calculated. This concept can be applied to patients aged18 years and older with either a diagnosis of ischemic stroke, TIA,intracranial hemorrhage, or at least one documented symptom consistentwith ischemic stroke, TIA, or intracranial hemorrhage that includesdocumentation of the presence or absence of each of the following:hemorrhage, mass lesion, and acute infarction.

Automated Analyzer—documentation of radiology exposure time in reportsfor procedures using fluoroscopy—This capability is based on the factthat the analyzer can decode numerical values. As an example method,using Stages 1-4 the percentage of final reports for procedures usingfluoroscopy that include documentation of radiation exposure or exposuretime can be calculated. As an example method, using Stages 1-4 toidentify the inappropriate use of “Probably Benign” assessment categoryin mammography screening the percentage of final reports for screeningmammograms that are classified as “probably benign” can be calculated.As an example method, using Stages 1-4 to identify in Nuclear Medicinethe correlation with existing imaging studies for all patientsundergoing bone scintigraphy by calculating the percentage of finalreports for all patients, regardless of age, undergoing bonescintigraphy that include physician documentation of correlation withexisting relevant imaging studies (e.g., x-ray, MRI, CT, etc.) that wereperformed. As an example method, using Stages 1-4 to identify stenosismeasurement in carotid imaging studies by calculating the percentage offinal reports for all patients, regardless of age, for carotid imagingstudies (neck MR angiography [MRA], neck CT angiography [CTA], neckduplex ultrasound, carotid angiogram) performed that include direct orindirect reference to measurements of distal internal carotid diameteras the denominator for stenosis measurement.

Automated Analyzer—Automated preliminary radiology report discrepancytracker—This capability is based on the analyzer decoding and analyzingdiscrepant reports when created by a non-attending radiologist. Thiswill ensure finalized impressions are reported to ordering physicianswill improve communication/hand-offs to ordering physicians and providean easy method to monitor further Quality Improvement in radiologyreport communication. As an example, report discrepancies (preliminaryreports) between radiology resident and radiology attending the resultscan be categorized and presented for tracking preliminary radiologyreport discrepancies to ensure finalized impressions are reported toordering physicians. As an example method, using Stages 1-4 to identifykey text describing a report as discrepant the entire PACS database canbe searched during a given period to analyze the frequency of discrepantreports. The results can be categorized according to post graduate yearin training to help residents and fellows identify areas of weakness forcontinued improvement, improve accuracy in preliminary reports, andimprove departmental and hospital administration confidence in the QA ofradiology resident reports.

An as example, analysis of the severity of discrepant resident reportswith respect to changes in patient management using a standard RadiologyPeer Review System (RADPEER) scoring system on a group of cases withdiscrepant diagnostic interpretations and to compare these data withpublished norms. In the RADPEER system, a score of 1 is defined asagreement between radiologists, a score of 2 is a difficult diagnosisnot expected to be made, a score of 3 is a diagnosis that should be mademost of the time, and a score of 4 is a diagnosis that should be madealmost every time. The overall percentage of discrepant reports can thenbe compared to published norms and further simplified into “under-reads”to “over-reads.”

Automated Analyzer—Key Performance Indicators (KPI)—This capability isbased on the analyzer calculating on-going metrics, or outcome measures,for achieving Quality Improvement (QI). Lapses in performance can haveconsequences of increased cost and increased patient morbidity andmortality. Often it is difficult to identify problems, consequences,implement changes, and reassess for improvement in a measurable fashion.Some common KPIs are: (A) Contrast reactions, screening, and treatment(B) Contrast extravasations, appropriate IV placement, follow-up, andtreatment (C) Pneumothorax after a procedure (as an example, the termspneumothorax, lung biopsy, and thoracentesis can be identified withStages 1-4) (D) Emergency Department Turn-Around-Times, emergencyimaging protocol completion rate (not all ordered imaging protocols arecompleted [refer to previous description of performance rate forrequested/written protocol A], read, and reported to the ED physicianwithin a given time frame), and stroke protocol imaging standardcompliance and (E) Report accuracy rates with recognition softwarereporting errors such as the words “ascending” and “descending” and “no”vs. “new”.

Overview of the Application of Bayes' Theorem within the AutomatedAnalyzer—All tests have limitations. Given a pre-test probability (asubjective measure) while factoring in the probability of the oppositeconclusion. Using this method, the probabilities are accounted for whilefactoring in the objective limits of the test in question given knowndata from prior validation studies. The result is a post-testprobability given this test specific objective data. The goal is toobjectify a subjective number to aid in the appropriate utilization of agiven test. Once the post-test probability is calculated you can make amore informed decision on using a particular test. Three judgment callsneed to be made prior to using this theorem: What is the pre-testprobability (as an example, 25%)? What is the cut-off to be applied tothe validation data of the test (an example, Intermediatecases+Positives cases=consider as positive)? What is your threshold foran outcome change (as an example, in the case of medical tests,treatment vs. no treatment)? (As an example, treatment only if post-testprobability is 80% or higher for disease). Overall, the result providesrealistic guidance for medical decision making, possibly in real-time.

Basic concepts used in medical diagnosis can be simplified into threesteps and applied within the Automated Analyzer—First, the symptomcomplex, or patient presentation. Second, medical knowledge is primarilyobtained through validation studies showing a given diagnosis and thespectrum of symptoms and signs that result. Third, the application ofSymbolic Logic (see below).

Two Mathematical Disciplines Used in Medical Diagnosis—Symbolic Logicand Probability as applied with the Automated Analyzer. For symboliclogic, the relationship between datasets is often illustrated with aVenn diagram in which sets are represented by regions in a plane. Fortwo sets S and T that are not separate and are not a subset of theother, the intersection can be described as /S∩T/ (refer to any standardmathematical text for a full description). This counting method iscalled the general addition rule for two sets where the yield is fourcombinations of attributes. For probability, the total probabilityequals the ratio of the number of patients with the attribute inquestion to the total number of randomly selected patients. Andconditional probability refers to the ratio is to a subpopulation ofpatients. The fundamental problem is as follows: Medical Knowledge istaught and understood as linear from diagnosis to symptoms and/or signs.However, during medical diagnosis the clinical thought pattern isbackwards and flows from symptoms and/or signs to diagnosis. Thistransition is accounted for by using Bayes' Theorem.

Assumptions required for application of Bayes' Theorem as applied withinthe Automate Analyzer—The flow from diagnosis to symptoms/signs inacquisition of medical knowledge is dependent on primarilypathophysiology. The pathophysiology data obtained from the medicalknowledge base (literature) can be extrapolated to the currentpopulation. Since symptoms/signs and diagnosis are binary attributes,the concept does not account for fuzzy logic, severity, time, location,or uncertainty. Incomplete penetrance is the norm for some attributes.Further the concept as described of 1 Dx (diagnosis)→1 Si/Sx(Sign/Symptom) assumes the Dx is within medical knowledge base.

Bayes' Probability as applied within the Automated Analyzer—Similar tothe Odds Method the goal is to objectify initial subjective data toproduce a result that may yield a significantly different outcome to aidclinical decision making (hereby reference to Lu, Ying. Advanced medicalstatistics. River Edge, N.J. [u.a.]: World Scientific, 2003, is nowincorporated for mathematical and statistical details). As an example,Posterior Probability=equals Total (prior) Probability multiplied by theConditional Probability divided by a normalizing constant. Further, theTotal Probability accounts for a population having a Dx regardless ofSi/Sx. The Conditional Probability accounts for the medical knowledgeconcept described previously (Dx→Si/Sx). Where the Normalizing Constantaccounts for objective data obtained from prior validation studies onthe given Dx→Si/Sx. And where the Posterior Probability (or probabilityof disease given the Si/Sx) equals

-   -   P(pos Dx|Si)=Si→Dx    -   Posterior Probability=Prior Probability×LR/Evidence.    -   If LR/Evidence˜LR*→then the following is true:    -   Posterior Probability=Prior Probability×LR*→then continue        updating with each new data point to yield new LR*        Additional Assumptions for Bayes' Thereon as applied with the        Automated Analyzer—Independence of attributes referring to        mutually exclusive, where:    -   1Si→1Dx    -   not 1Si→2 differ Dx        In the later example, it is required to combine the two        different diagnoses; otherwise Bayes' will not function. In        addition, the medical knowledge base is required to be        exhaustive, with little to no omissions. Further, the following        data must exist: validation data for the given diagnosis, the        probability or odds of each Si/Sx for the given diagnosis, and        the total probability if not starting from time equal to zero.

Bayes' Probability and Constant Adjustment as applied within theAutomated Analyzer—Both total and conditional probabilities are inconstantly under adjustment as older cases become irrelevant to thecurrent population. The current population is accounted for in the newset of objective data within the medical knowledge base.

Bayes' versus Scoring as applied within the Automated Analyzer—Both aremethods are binary, if not using Likard Scale (refer to a standardstatistical text for mathematical details). The primary difference isthat Bayes' has a prior probability component taking into account theclinical “feeling” or professional experience prior to factoring inobjective data. Example scoring methods are as described previously.

Bayes' versus Logic Tree Branching (or algorithm) as applied within theAutomated Analyzer—Logic Tree Branching is very similar when looked atas a series of individual decisions. The primary difference is thatLogic Tree Branching does not have a prior (pre-test) probabilitycomponent. Examples of Logic Tree Branching are as described previously.

Conditional Independence as applied within the Automated Analyzer—Whenthe assumption that the probability of observing the conjunction ofattributes is equal to the product of the individual probabilities ismade, the attributes can be described as having no relationship to eachother, as previously described.

Linear Regression as applied within the Automated Analyzer—The methodattempts to model the relationship between two variables by fitting alinear equation to observed data. One variable is an explanatoryvariable and the other is a dependent variable. Before attempting to fita linear model to observed data, a determination is made regarding ispresence of a relationship between the variables of interest. Therelationship does not imply a causative relationship, only a significantassociation between the two variables (refer to a standard statisticaltext for mathematical details).

Logistic Regression or Regression Analysis as applied within theAutomated Analyzer—The goal is to find a subset of all the explanatoryvariables that can be combined to predict the value of the outcomevariable. The outcome is a regression equation having the outcomevariable on the left hand side and a combination of the explanatoryvariables on the right. For any future patient, the values of theirexplanatory variables can be fed into the equation to predict the valueof their outcome variable. Further, logistic regression is a variationof ordinary regression which is used when the dependent (or response)variable is a dichotomous variable (requires only two values, whichusually represent the occurrence or non-occurrence of some outcomeevent, usually coded as 0 or 1) and the independent (input) variablesare continuous, categorical, or both (refer to a standard statisticaltext for mathematical details).

Comparison between Bayes' and Logistic Regression as applied within theAutomated Analyzer—Bayes' converges in O (log(n)) cycles, but theasymptomatic error is greater than Logistic Regression. Logisticregression converges in O(n) updates. Comparing both algorithms, thedifferentiating feature is that for a smaller number of examples Bayes'performs better, but for large number of examples Logistic Regressionperforms better (refer to a standard statistical text for mathematicaldetails).

Least-Squares Regression as applied within the Automated Analyzer—Amethod for fitting a regression line is the method of least-squareswhere minimizing the sum of the squares of the vertical deviations fromeach data point to the line is calculated. Because the deviations arefirst squared, then summed, there are no cancellations between positiveand negative values. The measures of correlation infer the same thing asmeasures in the study of regression (refer to a standard statisticaltext for mathematical details).

Solving the regression equation as applied within the AutomatedAnalyzer—The general regression equation can be written as y=a+b x. Inorder to predict a variable, the mean, variance, and standard deviationof the values of x and y need to be found. Mean is defined as theaverage value calculated by taking the sum of all values and dividing bythe total number of values. Variance is defined as the sum of squares ofdeviations from the mean, divided by the N. Standard Deviation isdefined as the square root of the average of the squared deviation(s)from the mean. Related concepts such as outliers, residuals, lurkingvariables, and avoidance of extrapolation, can also be employed (referto a standard statistical text for mathematical details).

Order entry support as applied within the Automated Analyzer—If theautomated analyzer is directed and set to extract meaning frompreviously described decoded reports and corresponding column categories(as examples, see following a to f) then the said described analysis canbe utilized to serve as appropriateness criteria before an imaging orderis requested 200 (refer to FIG. 3). The said method allows additionaldecision making support at the time of imaging order request 200:

-   -   a. Demographics (DEMO)    -   b. V/S—example of automated BP analysis    -   c. Si/Sx—example of a variety of pain locations, and signs such        as unequal BP in the upper extremities with automated analysis        from the EMR.    -   d. PMH—examples of automated analysis on medical syndromes such        as Marfan's and Ehler Danlos, as well as a variety of surgical        aortic repairs and valve repairs.    -   e. EKG—example of automated analysis of associated EKGs. This        also demonstrates the NLP/decoder can be run on non-imaging        tests to extract meaning    -   f. CXR—Another example of NLP/decoder run on a different        radiology examination.    -   g. Surgical reports and notes

Decision support at the time of report creation—If the automatedanalyzer is directed and set to extract meaning from previouslydescribed decoded reports and corresponding column categories withapplication of dynamic report decoding and subsequent analytics prior toimaging report sign-off, said analyzer will serve as real time decisionmaking support for reading radiologist (and to assist in properreporting for billing purposes) in the context of a primary finding,alternative finding, and/or critical finding.

Decision support at the time of report creation—If the automatedanalyzer is directed and set to extract meaning from previouslydescribed decoded reports and corresponding column categories withapplication of dynamic report decoding and subsequent analytics prior toimaging report sign-off, said analyzer will serve as real time decisionmaking support for reading radiologist (and to assist in properreporting for billing purposes) in the context of potential or possibletextual errors.

Decision support at the time of report creation—If the automatedanalyzer is directed and set to extract meaning from previouslydescribed decoded reports and corresponding column categories withapplication of dynamic report decoding and subsequent analytics prior toimaging report sign-off, said analyzer will serve as real time decisionmaking support for reading radiologist (and to assist in properreporting for billing purposes) in the context of suggested reportstructure according to a pre-determined database.

In general, any goal can divided into component parts and processedusing Stages 1-4:

-   -   GOAL: any area of concern can be searched (KPI) (STAGE 1)    -   RESTRUCTURE AND PARSE DATA (STAGE 2)    -   RUN NLP ENGINE TO DECODE DATA (STAGE 3)    -   APPLY AUTOMATED ANALYZER (STAGE 4)    -   CHANGE PROCESS: based on results from automated analyzer    -   REDO GOAL SEARCH: same area of concern can be searched again        (KPI) (STAGE 1)    -   RESTRUCTURE AND PARSE DATA (STAGE 2)    -   RUN NLP ENGINE TO DECODE DATA (STAGE 3)    -   APPLY AUTOMATED ANALYZER (STAGE 4): are results improved from        after process change?

It is to be understood, of course, that while the exemplary methodsdetailed above are performed manually, the methods themselves can beperformed in a more automated fashion by writing a simple script orother piece of code. Thus the methods can be combined into a singlebroad software application thereby achieving natural languageunderstanding and auto analytics.

And, it is to be understood, of course, that all of the above methodsmay be validated using improved reference standards as they becomeavailable from improved verified diagnostics in imaging, pathology, orlaboratory analysis hereby allowing production of contingency tables (asan example, a process for validation of each imaging protocol).

In the foregoing description, the method and apparatus of the presentinvention have been described with reference to specific examples. It isto be understood and expected that variations in the principles of themethod and apparatus herein disclosed may be made by one skilled in theart and it is intended that such modifications, changes, andsubstitutions are to be included within the scope of the presentinvention as set forth in the appended claims. The specification and thedrawings are accordingly to be regarded in an illustrative rather thanin a restrictive sense.

What is claimed is:
 1. A method for the display of radiology, clinical,pathology, and laboratory reports in a graphical or tabular format, themethod comprising the steps of: optimizing a keyword search within akeyword searchable database; restructuring and parsing text data in thedatabase; creating and applying a natural language processing engine tothe database; and applying a comprehensive automated analyzer to thenatural language processed database, wherein a result from the automatedanalyzer can then be viewed in the graphical or tabular format on one ofa computer or hand-held device.
 2. The method according to claim 1,wherein the step of optimizing a keyword search within the keywordsearchable database further comprises the step of: using bootstrappingand jackknifing techniques on at least one of: a radiologist (physician)verified and confirmed reference standard to validate keyword searchcriteria for each imaging protocol; a pathologist (physician) verifiedand confirmed reference standard to validate keyword search criteria foreach diagnostic protocol; a clinician (physician) verified and confirmedreference standard to validate keyword search criteria for eachdiagnosis; and, a clinician (physician) verified and confirmed referencestandard to validate keyword or key-number search criteria for eachlaboratory protocol and parameter.
 3. The method according to claim 1,wherein the step of optimizing a keyword search within the keywordsearchable database further comprises the steps of: improving theaccuracy of the search results by using optimized boolean and powersearch techniques for queries based on at least one of: the imagingprotocol in question; the diagnostic protocol in question; the diagnosisin question; the laboratory protocol or laboratory parameter inquestion; and repeatedly applying the search techniques to continuouslyimprove the accuracy of the keyword search criteria to further optimizeresults in an effort to retrieve all records with true hits (relevanthits) while minimizing retrieval of false hits based on confoundingvariables.
 4. The method according to claim 1, wherein the step ofoptimizing a keyword search within the keyword searchable databasefurther comprises the step of uploading the optimized results (recordsor reports) to a server for further processing.
 5. The method accordingto claim 1, wherein the step of restructuring and parsing text data (oralphanumeric string) into the database further comprises the steps of:using a find/replace function to delete tabs, paragraphs, and colons intext; rebuilding paragraphs, or text data (or alphanumeric string), tocreate rows within a spreadsheet; reprocessing the same string of textdata (or alphanumeric string) to create delimiters as placeholders forparsing the data into separate cells on the same row; uploading andimporting the rebuilt and processed text data (or alphanumeric string)into a spreadsheet application; directing the spreadsheet application toselect the appropriate delimiters and text style; directing thespreadsheet application to select the appropriate pre-definedalphanumeric strings and text style; directing the spreadsheetapplication to batch replace pre-defined alphanumeric strings to allowstandardization of report format; dynamically restructuring and parsingreports into text data (or alphanumeric string) (or alphanumericstrings) within cells across columns whereby the source text data (oralphanumeric string) is matched to a given row and the column content ismatched as described by the column headings; dynamically enabling the“shrink to fit” feature and applying said feature to the text data (oralphanumeric string) (or alphanumeric strings) within a spreadsheet ordatabase; dynamically using the “shrink to fit” feature when enabled asapplied to text data (or alphanumeric string) (or alphanumericstring(s)) to analyze a spreadsheet or database efficiently; dynamicallyenabling the “fill color” feature, or background cell color, andapplying said feature to all cells, and specifically only those cells,containing text data (or alphanumeric string) (or alphanumeric strings)within a spreadsheet or database; identifying cells within a spreadsheetor database by color coding to enable downstream processing or visualprogramming; associating cells containing text data (or alphanumericstring) (or alphanumeric strings) using said color coding; assigning agiven background cell color to identify or classify the text data (oralphanumeric string) (or alphanumeric string(s)) contained within saidcell; assigning a given background color to identify or classify theabsence of text data (or alphanumeric string) (or alphanumericstring(s)) contained within cell; dynamically comparing the backgroundcolor of a cell to another cell to generate or derive knowledge orunderstanding; matching of text data (or alphanumeric string) (oralphanumeric string(s)) contained within a cell, row, and/or column to adescriptive header term within a spreadsheet or database; classifying oftext data (or alphanumeric string) (or alphanumeric string(s)) containedwithin a cell, row, and/or column to a descriptive header term within aspreadsheet or database; removing of PHI from said database insituations where de-identified patient data is required; and dynamicallyapplying the said methods above in claim 2 to successive cells, rows,and/or columns of text data (or alphanumeric string) (or alphanumericstring(s)) in a spreadsheet or database.
 6. The method according toclaim 1, wherein the step of creating and applying a natural languageprocessing engine to a database further comprises the steps of:uploading a generic batch find/replace application capable of executingsuch task on at least one of a text file, digital document, spreadsheet,and database; applying said generic batch find/replace application to atext data (or alphanumeric string) (or alphanumeric string(s)) database;generating key text phrases, text data (or alphanumeric string) (oralphanumeric string(s)) verified as valid by a qualified professionalrepresentative to serve as the basis of a decoder table; matching thekey text phrases or text data (or alphanumeric string), with or withoutthe use of single or multi-character wildcards, to generate a decodetable, decode columns, translation table, or translation columns;identifying numerical values as how far away, in single characterincrements within an alphanumeric string in a cell, they are from akeyword such as aneurysm (or positive AAD) to denote a level ofconfidence in the identified value being associated with said keyword;and, dynamically generating similar text phrases (or alphanumericstring(s)) in a repetitive manner using one of a manual input andfunction (formula) derivation.
 7. The method according to claim 6,further comprising at least one of the steps of: analyzing thedynamically generated text phrases, manually or artificially derived, bya qualified professional representative to serve as the basis of adecoder table; and analyzing the dynamically generated text phrases,manually or artificially derived, by a qualified professionalrepresentative to ensure accuracy of the decoder table or translationtable.
 8. The method according to claim 7, further comprising the stepsof: generating at least one of a secondary color coding, a cell fillcolor, a background fill color, a text font or a style change to keytext phrases (text data (or alphanumeric string) or alphanumericstring(s)) to serve as the basis of a decoder table; matching the atleast one secondary color coding, cell fill color, background fillcolor, text font or style changes to the key text phrases to generate atleast one of a decode table, decode columns, translation table, ortranslation columns; and, dynamically generating at least one of asimilar secondary color coding, cell fill color, background fill color,text font or style change to key text phrases in a repetitive mannerusing at least one of manual input or function (formula) derivation. 9.The method according to claim 8, further comprising the steps of:analyzing such dynamically generated secondary color coding, or cellfill color, or background fill color, and/or text font or style changesto key text phrases, manually or artificially derived, by a qualifiedprofessional representative to serve as the basis of a decoder table;and, analyzing such dynamically generated secondary color coding, orcell fill color, or background fill color, and/or text font or stylechanges to key text phrases, manually or artificially derived, by aqualified professional representative to ensure accuracy of the decodertable or translation table.
 10. The method according to claim 9, furthercomprising the steps of: dynamically applying each of the previous stepsin an iterative fashion using at least one of: a slight variation of thedecode or translation alphanumeric string used at each step; and abinary decision making step before execution of the next step (if cell Ais decoded as positive then proceed to application of decode table B tothe adjacent cell), wherein the successive steps of both text data (oralphanumeric string) decoding and application of secondary color codingprovides a level of natural language understanding, inherent QualityAssurance, validation, and verification; and, dynamically changing thealgorithm that decides which decoding step occurs in succession relativeto the answer or result from the previous decoding step.
 11. The methodaccording to claim 10, further comprising the steps of: dynamicallydecoding reports (or alphanumeric string(s)) for proper or completeperformance, or performance as designed, of a diagnostic protocol basedon a given reference; identifying decoded diagnostic reports ascorrectly performed or incorrectly performed as designed; classifyingdecoded diagnostic reports as correctly performed or incorrectlyperformed as designed; and, reclassifying incorrectly performeddiagnostic reports to an appropriate classification.
 12. The methodaccording to claim 11, further comprising the step of: dynamicallydecoding diagnostic reports, or other reports, as positive, negative, orindeterminate for a diagnosis or disease.
 13. The method according toclaim 11, further comprising the step of: dynamically decodingdiagnostic reports, or other reports, as positive, negative, orindeterminate, based on a calculated mean or statistical numericalaverage of a given number of similarly decoded cells for a singlereport, for a diagnosis or disease.
 14. The method according to claim11, further comprising the step of: dynamically decoding diagnosticreports, or other reports, as according to known subtypes (subcategoriesor subclasses, both mutually exclusive and/or non-mutually exclusive)for a diagnosis or disease.
 15. The method according to claim 11,further comprising at least one of the steps of: dynamically decodingdiagnostic reports, or other reports, as according to known subtypes(subcategories or subclasses, both mutually exclusive and/ornon-mutually exclusive) for a diagnosis or disease: based on acalculated mean or statistical numerical average of a given number ofsimilarly decoded cells for a single report; or by assigning secondarycolor coding as generated by superimposed NLP (or automatedunderstanding); dynamically decoding diagnostic reports, or otherreports, as according to known subtypes (subcategories or subclasses,both mutually exclusive and/or non-mutually exclusive) based on acalculated mean or statistical numerical average of a given number ofsimilarly decoded cells for a single report, for a diagnosis or disease,by assigning secondary color coding as generated by superimposed NLP (orautomated understanding); dynamically decoding diagnostic reports, orother reports, as according to known subtypes (subcategories orsubclasses, both mutually exclusive and/or non-mutually exclusive) for adiagnosis (or alternative diagnosis), disease, or finding within asingle cell (or examination) by assigning secondary color coding asgenerated by superimposed NLP (or automated understanding), therebyrepresenting a variably described single finding versus variablydescribed additional finding(s), the dominant finding (or finding withthe greatest number of decoded hits) can be represented by saidsecondary color coding; dynamically decoding diagnostic reports, orother reports, as according to known subtypes (subcategories orsubclasses, both mutually exclusive and/or non-mutually exclusive) for adiagnosis (or alternative diagnosis), disease, or finding within asingle cell (or examination) based on a calculated mean or statisticalnumerical average of a given number of similarly decoded cells for asingle report, thereby representing a variably described single findingversus variably described additional finding(s), the dominant finding(or finding with the greatest number of decoded hits); dynamicallydecoding diagnostic reports, or other reports, as according to knownsubtypes (subcategories or subclasses, both mutually exclusive and/ornon-mutually exclusive) for a diagnosis (or alternative diagnosis),disease, or finding within a single cell (or examination) by assigning adifferent secondary color coding as generated by superimposed NLP (orautomated understanding), thereby representing a variably describedabsence/lack of a single finding versus variably described absence/lackof an additional finding(s), the dominant finding, or lack thereof (orfinding with the greatest number of decoded negative hits) can berepresented by said secondary color coding; dynamically decodingdiagnostic reports, or other reports, as according to known subtypes(subcategories or subclasses, both mutually exclusive and/ornon-mutually exclusive)) for a diagnosis (or alternative diagnosis),disease, or finding within a single cell (or examination) based on acalculated mean or statistical numerical average of a given number ofsimilarly decoded cells for a single report, thereby representing avariably described absence/lack of a single finding versus variablydescribed absence/lack of an additional finding(s), the dominantfinding, or lack thereof (or finding with the greatest number of decodednegative hits); dynamically decoding diagnostic reports, or otherreports, as according to known subtypes (subcategories or subclasses,both mutually exclusive and/or non-mutually exclusive) for a diagnosis(or alternative diagnosis), disease, or finding within a single cell (orexamination) by assigning a different secondary color coding asgenerated by superimposed NLP (or automated understanding), therebyrepresenting a variably described stability (versus absence/lack) of asingle finding versus variably described stability (versus absence/lack)of an additional finding(s), the dominant finding, or lack thereof (orfinding with the greatest number of decoded negative hits) can berepresented by said secondary color coding; dynamically decodingdiagnostic reports, or other reports, as according to known subtypes(subcategories or subclasses, both mutually exclusive and/ornon-mutually exclusive) for a diagnosis (or alternative diagnosis),disease, or finding within a single cell (or examination) based on acalculated mean or statistical numerical average of a given number ofsimilarly decoded cells for a single report, thereby representing avariably described stability (versus absence/lack) of a single findingversus variably described stability (versus absence/lack) of anadditional finding(s), the dominant finding, or lack thereof (or findingwith the greatest number of decoded negative hits); dynamically decodingdiagnostic reports, or other reports, as according to known subtypes(subcategories or subclasses, both mutually exclusive and/ornon-mutually exclusive) for a diagnosis (or alternative diagnosis),disease, finding, finding location (location within the human body),within a single cell (or examination) based on an identified numericalvalue (measurement or described alphanumerical size) by assigning eithera predefined category (value, classification, absence or presence ofdisease, or other alphanumerical string) or different secondary colorcoding as generated by superimposed NLP (or automated understanding),thereby representing a variably described presence or stability (versusabsence/lack) of a single finding versus variably described presence orstability (versus absence/lack) of an additional finding(s), thedominant finding, or lack thereof (or finding with the greatest numberof decoded positive or negative hits) can be represented by an assignedcategory (as example, value identified meets definition of aneurysmalsize) or a secondary color coding; dynamically decoding diagnosticreports, or other reports, as according to known subtypes (subcategoriesor subclasses, both mutually exclusive and/or non-mutually exclusive)for a diagnosis (or alternative diagnosis), disease, finding, or findinglocation (location within the human body) within a single cell (orexamination) according to a calculated mean or statistical numericalaverage of a given number of similarly decoded cells for a singlereport, based on an identified numerical value (measurement or describedalphanumerical size) by assigning either a predefined category (value,classification, absence or presence of disease, or other alphanumericalstring) or different secondary color coding as generated by superimposedNLP (or automated understanding), thereby representing a variablydescribed presence or stability (versus absence/lack) of a singlefinding versus variably described presence or stability (versusabsence/lack) of an additional finding(s), the dominant finding, or lackthereof (or finding with the greatest number of decoded positive ornegative hits) can be represented by an assigned category (as example,value identified meets definition of aneurysmal size) or a secondarycolor coding; dynamically applying above said secondary color coding tothe concept of NLP engine decoding yields thereby allowing efficienthuman processing or visual programming; and, dynamically applying atleast one of the previous steps to successive cells, rows, and/orcolumns of text data (or alphanumeric string) (or alphanumericstring(s)) in a spreadsheet or database.
 16. The method according toclaim 1, wherein the step of creating and applying a comprehensiveautomated analyzer to a natural language processed database furthercomprises the steps of: analyzing, either dynamically or in a staticmanner, a decoded database using a variety of mathematical functions andformulas; and, sending the analytics results to a different variety ofmathematical functions and formulas for additional processing, eitherdynamically or in a static manner.
 17. The method according to claim 16,further comprising the step of analyzing a decoded database using amathematical function and/or formula for at least one of: a positivityrate; a negativity rate; a number of positive scans; a number ofnegative scans; a sample size determination; an imaging protocolexaminations for a given time period; and, an imaging protocolverification.
 18. The method according to claim 16, further comprisingthe step of analyzing a decoded database using a mathematical functionand/or formula for at least one of: an appropriateness gauge; an imagingprotocol performance gauge; a requested/written imaging protocolpositivity gauge; an imaging protocol positivity gauge; an imagingprotocol overall positivity gauge; an imaging protocols gauges; animaging protocol performance gauge; an imaging protocol positivitygauge; an imaging protocol overall positivity gauge; and, a referrerpositivity gauge for imaging protocol.
 19. The method according to claim16, further comprising the step of analyzing a decoded database using amathematical function and/or formula for at least one of: a radiologydepartment positivity gauge for imaging protocol; a regional or citypositivity rate for imaging protocol; a positivity rate difference; animaging protocol utilization rate difference between a first and asecond hospital; and, a utilization difference of imaging protocolbetween a first and a second hospital.
 20. The method according to claim16, further comprising the step of analyzing a decoded database using amathematical function and/or formula for at least one of: a totalpositivity rate difference between a first and a second radiologist forimaging protocol; an imaging protocol positivity rate for a departmentover a given time period compared to radiologist ROC; an imagingprotocol positivity rate for a department over a given time periodcompared to ordering healthcare professional utilization; an overallaccuracy for a diagnosis, for a given radiologist; a summative ROCcurve, and overall accuracy for a diagnosis, for a given radiologist; anoverall accuracy a given for department; and, an imaging protocol costs,billing, or coding.
 21. The method according to claim 16, furthercomprising the step of analyzing a decoded database by application of atleast one of: Bayes' theorem; symbolic logic; probabilities; scoring;conditional independence; logistic regression; linear regression;least-squares regression, and related concepts (such as outliers,residuals, and lurking variables); extrapolation; a loss matrix; and, anopportunity loss matrix, wherein such application dynamically generatesmore informed decision making process.
 22. The method according to claim16, further comprising the step of analyzing a decoded database byapplication of at least one of: a test cost, a test sensitivity, a testspecificity, a diagnosis (or disease), a differential causative agentfor said diagnosis (or disease), probabilities, total populationprobabilities, accuracy, predicative values, and expected values of saidtest within the concept of a loss matrix and an opportunity loss matrix,wherein such application dynamically generates more informed decisionmaking process.
 23. The method according to claim 16, further comprisingthe step of application of dynamic report decoding and subsequentanalytics prior to at least one of: imaging order and/or at the time ofimaging order request to serve as real time decision making support;imaging report signoff to serve as real time decision making support forreading radiologist (and to assist in proper reporting for billingpurposes) in the context of a primary finding, alternative finding,and/or critical finding; imaging report signoff to serve as real timedecision making support for reading radiologist (and to assist in properreporting for billing purposes) in the context of potential or possibletextual errors; and, imaging report signoff to serve as real timedecision making support for reading radiologist (and to assist in properreporting for billing purposes) in the context of suggested reportstructure according to a pre-determined database.
 24. The methodaccording to claim 1, wherein the previous steps are applied tosuccessive cells, rows, and/or columns of text data in at last one of aspreadsheet or database.
 25. An apparatus for receiving, inputting, anddelivering commands and instructions to display radiology, clinical,pathology, and laboratory reports in a graphical or tabular format, theapparatus comprising: an Automated Analyzer server database; at leastone data entry port through which a variety of data may be added to theAutomated Analyzer server database; an NLP engine decoder whichinteracts with the Automated Analyzer database; and at least one of alocal workstation or a mobile device capable of having an AutomatedAnalyzer client application downloaded into its memory, wherein thelocal workstation or mobile device is capable of displaying theAutomated Analyzer client, and wherein the local workstation or mobiledevice is capable of displaying the delivered graphical or tabularformat representation of the results from the Automated Analyzer serverdatabase.
 26. The apparatus according to claim 10, further comprising acomputer system where the Automated Analyzer client and deliveredtabular or graphical representation of the results are available via apassword protected user interface (window, browser, or other, etc.) foruser access and where the information may be viewed or printed out.